Food security is an important foundation of national security, and the fundamental of guaranteeing national food security lies in arable land protection. In order to realize the protection of arable land in the context of ecological civilization, this paper designs a governance framework for arable land protection based on supply, empowerment, and control on the basis of the trinity of arable land protection policy of quantity, quality, and ecology, and constructs a multi-objective land use structure optimization model, and obtains a scenario prognosis for the optimal allocation of the land use structure by using a hybrid genetic algorithm. Taking County A as the specific research object, it can be seen by predicting the land use structure under the natural development scenario of County A that, relative to the status quo in 2023, the predicted share of arable land in 2050 has the largest decrease of 0.49%, and the shares of garden land, forest land, grassland and water area have all decreased, which is mainly converted into construction land (increased by 0.78%). From the Pareto frontier solution of land ecological benefit objective and economic benefit objective, three typical schemes of land use structure optimization were obtained, among which, the optimization scheme of balanced development of economy and ecology balanced economic and ecological development balanced economic development and ecological protection, and was selected as the optimization scheme of this paper. The increase of arable land area in this scheme is 0.38%, much higher than the -2.46% in the unoptimized case, which is in line with the requirement of arable land retention and can be used as a reference for further optimization of arable land protection framework.

The popularity of network video service, which leads to the specification of the network video service quality is becoming more and more urgent, and human is the ultimate watch users of network video, Evaluation of human observers on the video’s perception of the situation is becoming more important, depending on the source video network video know nothing, you will need to refer to the participation of video quality evaluation algorithm. However, when the quality evaluation is done by human rating, it is time-consuming and laborious, so the computer is required to make an objective evaluation of the video. In the objective evaluation, the excellent performance of convolutional neural network based on deep learning in feature extraction contributes to the rapid development of the research field of video quality evaluation. However, the development of deep learning algorithms requires appropriate data sets for training and testing. The existing data sets are relatively small in scale and not comprehensive in terms of video content types and distortion types. Therefore, it is necessary to provide a new data set to evaluate the quality of video without reference, expand the scale of the data set, expand the content and distortion type of the video. At the same time, considering the new development of network video services, the video resolution is positioned as high definition, and the original video sampling ratio is 4:2:2. The dataset is freely available to relevant researchers for scientific research.

Graph neural networks are an effective method for action recognition using human skeletal data, but previous recognition methods lack attention to spatial features. In order to improve this research deficiency, this paper conducts action recognition research based on ST-GCN. An action recognition network based on two-way skeletal joint information is proposed, where the human body is divided into various parts to calculate the representation vectors, and a graph convolutional neural network is trained to obtain the classification results. Attention mechanism is designed to minimize the effect of background noise, and data enhancement by means of flipping and shifting is performed to improve the model performance. Ablation experiments verify high accuracy when using both the attention mask matrix and the global self-attention mechanism, as well as when using both the joints network branching and the parts network branching. The model in this paper recognizes all 12 actions of the NW-UCLA dataset with accuracies higher than 92%, and the data enhancement effect is also verified.

Based on the research of digital economy and digital transformation of manufacturing industry, the article constructs the evaluation index system of digital economy development level and digital transformation of manufacturing industry in Yangtze River Delta respectively. The entropy weight TOPSIS method is used to measure and analyze the level of digital economy development and the level of digital transformation of manufacturing industry in the Yangtze River Delta respectively. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta in general and in each province and city is calculated and analyzed. On the basis of the relevant conclusions, recommendations for the digital transformation of the manufacturing industry and the elimination of the “digital divide” in the Yangtze River Delta are proposed. The overall trend of digital economy in the Yangtze River Delta (YRD) is on the rise, but there is a significant “digital divide”. The increase in the level of digital economy development in the Yangtze River Delta over the past 10 years is 10.76%. The average value of digital economy development water in Shanghai, Jiangsu, Zhejiang and Anhui is 0.358, 0.549, 0.491, 0.185 respectively, and the development of digital economy in Anhui is insufficient. Shanghai’s manufacturing digitalization water average is 0.750, the highest level in the Yangtze River Delta. Zhejiang and Anhui are slightly behind in manufacturing digital transformation. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta grew from 0.592 to 0.879, and the type of coupling coordination development shifted from barely coordinated to well coordinated. Shanghai and Jiangsu have reached the good coordination level. Zhejiang is at the intermediate coordination level. And Anhui is at the barely coordinated stage, which is the lowest level in the Yangtze River Delta.

Artificial Intelligence has been applied in many aspects of life, however, AI algorithms have been less used in the field of music. In this paper, a multi-track based pop music generation model MuseGAN is proposed, due to its poor contextualization and excessive tempo jumps in generating pop music samples. In this paper, a new multi-track pop music generation model-Recurrent Feature Generation Adversarial Network RFGAN is proposed. the model addresses the temporal relevance of the music structure and the repetitive nature of the musical section, and proposes a temporal model that enhances the contextual relevance of the music samples in terms of the time series, and improves the generative model according to this temporal model by converting the unidirectional structure in the original model to a recurrent structure, adding the feature extractor to the previous level of training information, which is combined with arbitrary noise and passed to the next training. An average pooling layer is added at the end of the generative model as a solution to the situation where the model generates too much noise for pop music samples. The improved model is superior to the pre-improvement model in terms of stability, convergence speed, and overfitting in pop music generation. In the audience scoring experiment, 60% of the top 5 pop music scores were generated using the RFGAN model proposed in this paper, indicating that the pop music generated using the RFGAN model has reached a high level comparable to the level of artificial pop music composition.

Groundwater seepage has a greater impact on the stability of foundation engineering, and it is also an important factor that restricts the development of geotechnical engineering projects and the quality of engineering surveys. In this paper, tunnel engineering is selected as the foundation engineering project under study to investigate the specific influence of groundwater seepage in the process of tunnel excavation. The flow-solid coupling model is constructed, and the safety coefficients of the tunnel project in different situations are calculated based on the strength discount method and the ultimate strain method. Numerical analysis software is used to establish the calculation model of the influence of groundwater seepage on the stability of tunnel excavation. The displacement of surrounding rock around the hole is selected as the evaluation index of tunnel stability, and the effect of groundwater seepage on each index is calculated by the analysis software. The study shows that groundwater seepage will make the rock body around the palm face after tunnel excavation change significantly from the pre-excavation bending. The seepage increases the displacement of the surrounding rock, and the coefficient of increase of vertical displacement is larger than the coefficient of increase of horizontal displacement. At the same time, the flow-solid coupling effect of groundwater seepage increases the surrounding rock stress, and the increase coefficients of each key point of the tunnel excavation are between 1.11-1.50, resulting in significant deformation of the bottom of the arch, the top of the arch, and the arch girdle. In addition, the groundwater seepage makes the tunnel safety coefficient decrease from 9.03 to 5.18, which significantly causes the decrease of tunnel stability.

In the current business environment, artificial intelligence is becoming a key force driving performance management and organizational change. In this paper, finance, customer, internal process, learning and growth are selected as the indicators of performance evaluation of Enterprise A through the balanced scorecard model, and the fuzzy algorithm is used to provide comprehensive scores and grades for the above indicators. In addition, this paper sets up an organizational structure change evaluation model and analyzes the effect of the system on the organizational change of Enterprise A through the measurement of key indicators. The recognition rating of the questionnaire set up in this paper by the employees of Enterprise A is 4.15, and the recognition degree of the Intelligent Performance Management System is “basically recognized”. The intelligent performance management system improves the design and execution of internal processes in Enterprise A, and promotes the organizational change of Enterprise A in terms of total resource utilization. In conclusion, this study provides reliable technical support for enterprise performance management and organizational change.

Colleges and universities are an important part of higher education, providing a large number of talents for social development. The study optimizes the way of student management in colleges and universities based on artificial intelligence technology. Firstly, the K-means algorithm in cluster analysis is used to classify students’ campus behavioral characteristics. Then use Apriori algorithm to correlate students’ behavioral characteristics with academic performance. Finally, colleges and universities can take differentiated management measures for different categories of students. The clustering analysis of 12,885 students’ consumption behavior, work and rest behavior, and study behavior in college Z, followed by the correlation analysis between the clustering results and academic performance, and a total of 10 correlation rules were found. Colleges and universities can formulate management rules based on the analysis results to improve management efficiency. In addition, the student management work of colleges and universities can be optimized and upgraded in several directions, including the awareness of student management work in colleges and universities, the information platform, the archive management work, the student management team, and the information security work.

Under the background of the current information age, the electronic and intelligent transformation of the bidding industry has become an inevitable trend, and the e-bidding model stands out and greatly improves people’s understanding of bidding. Aiming at the traditional e-bidding system, in order to solve the problem of the lack of the traditional e-bidding system that provides the bidding body with referable opinions, this study firstly constructs the e-bidding risk assessment indexes and realizes the optimization of the evaluation module of the system. Then the recommendation algorithm based on deep learning implements the optimization design of the e-header bidding system. This study constructs an optimized recommendation model by fusing knowledge graphs on the basis of deep learning. Then the e-tendering optimization system is designed according to the actual needs of e-tendering, combined with the recommendation model of this paper. The accuracy index ACC of this paper’s recommendation model is improved by about 3% on average compared with other best-performing recommendation models on each dataset, which verifies the excellent performance of this paper’s recommendation algorithm. This study constructs an optimized e-tendering system and proposes suggestions for the development and operation strategy of corporate e-tendering, contributing to the development of e-tendering transactions and the participation of social capital.

The flipped classroom relies on a smart platform to assist the implementation of English translation teaching, combining the smart platform with the students as the core to realize the efficient interaction of English translation teaching and enhance the students’ interest in English translation learning. This paper develops an easy-to-use interactive system for English translation teaching in flipped classroom based on Fine Report, and utilizes MySQL database to store the relevant data generated in the process of use. In this system, the BERT model trained by matrix masking strategy is used as the basis, and the neural machine translation model that assists teachers in English translation homework correction is established by combining the NMT model. Then the K-Means clustering algorithm is optimized by the adaptive K-value selection method, and the students’ learning data on the system is clustered by using the improved K-Means, and the student performance evaluation model is established by combining the CART decision tree. A pedagogical comparison experiment was carried out for the feasibility of the interactive system for teaching English translation in the flipped classroom. The BLUE value of machine translation using the BERT-NMT model was always above 30, and the average accuracy of student performance prediction of the K-Means-CART model could reach 84.85%. The English translation performance of the students in the experimental class was significantly improved after the teaching experiment, and the overall satisfaction of the students with the interactive system for teaching English translation was 4.038 points, which was between the satisfied~very satisfied level. Fully combining intelligent technology to assist teachers in teaching English translation under the flipped classroom can help to enhance the quality of cultivating English translation talents in colleges and universities.

When a laser beam passes through a solid physical material with a nonlinear refractive index, it can produce an optical nonlinear effect, which depends on the refractive index that changes with the light intensity. Based on an analysis of the linear principle of nonlinear optics, the article describes the coupled wave equations under the nonlinear optical phenomenon. It introduces the phase-matching method of frequency conversion and the theoretical basis of optical waveguide. Starting from the classical Maxwell’s equations, the nonlinear optical transmission equations and the optical effect model are established, and then the finite element method (FEM) simulation model is constructed based on the FEM model to analyze the nonlinear optical phenomena of solid-state physical silicon materials. To verify the validity of the FEM model, the optical bistability effect and four-wave mixing spectrum of the nonlinear optical phenomena are simulated and analyzed, and the homochiral spinning effect and transmission spectrum are investigated. When the solid-state physical silicon material is rotating, the laser power required to observe the optical bistability is up to 9.51 W when the rotation rate is increased from 12 kHz to 24 kHz, and the four-wave mixing intensity decreases from 0.115 to about 0.028 when the oscillator frequency of the solid-state physical silicon material is increased from 15 MHz to 30 MHz. The plasma resonance absorption wavelength of the solid physical silicon material is at 791 nm, and the effective refractive index obtained from the simulation is 0.61 in the real part, which is only 1.64% lower than the actual refractive index. The trend of nonlinear optical phenomena in solid-state physics can be effectively obtained by using the FEM model, which provides a new idea for the application expansion of the optical force system.

With the development of the informationization era, it has become the norm for teachers of Civics and Political Science courses in colleges and universities to assist classroom teaching through network resources. In order to further utilize network resources to make them better serve the classroom teaching of Civics and Politics courses in colleges and universities, this paper optimizes the teaching resources recommendation technology based on deep neural network. Defining the network teaching resources data as a ternary group , we put forward the research hypothesis and LSTM model, and establish the G-LSTM recommendation model for recommending the teaching resources of ideological network. The overall framework of G-LSTM model is described, and the recommendation based on G-LSTM is applied to the ideological network teaching resources recommendation. Adopt AUC, MRR and NDCG as evaluation indexes to check the performance indexes of G-LSTM model. Combined with the actual teaching of ideologic theory class, the practical effect of G-LSTM recommendation model is analyzed. 67.81% of students and 39.71% of teachers recognize each recommended online teaching resources. It shows that the improved LSTM model in this paper can further screen the ideological and political network teaching resources, and the teaching resources recommended by the model are more suitable for the teaching of ideological and political theory.

Machine learning-based learning analytics can fully use the learner learning behavior interaction data recorded by online English teaching systems, providing support for observing students’ learning process from the perspective of learning behavior. In this paper, we construct a framework for recognizing college students’ English learning behavior patterns, propose an SGT-based feature extraction algorithm for learning sequences, and use Gaussian mixture models to identify the extracted learning characteristic sequences. Subsequently, a K-means clustering algorithm is used for sequence clustering and lag sequence analysis. At the same time, the English personalized teaching method is designed by combining the proposed personalized knowledge point recommendation method of multi-knowledge fusion in-depth knowledge tracking and group feature collaborative filtering. The results show that college students’ English learning behaviors are classified as active, passive, and passive, and the behavioral sequences of students in different modes are differentiated, in which the sequence residual value of active learners is greater than 1.96. There is a significant difference between the personalized teaching mode and the ordinary teaching mode in terms of the learning mode and the learning effect (P<0.05), and it can achieve a better English teaching effect.

The development of media technology profoundly affects the presentation mode, dissemination rate and scale of news information, which in turn reshapes the business chain and business landscape of the entire news media industry. Based on the analysis of the shortcomings of the LDA model, this paper proposes an improved LDA model with binomial distribution, and applies it to the analysis of the evolution of news topics. The model introduces binomial distribution to enhance the discriminative ability of lexical items, and parallelises it to improve the classification effect of news topics. In order to effectively obtain the relevant features of cultural communication in news documents, this paper introduces BERT to obtain word embedding and word vector matrix, and then realises the generation of theme word structure and theme words. The performance of the improved LDA model is verified through the THUCNews dataset, and the news topic morphology is visualised and analysed with the example data, and its morphological evolution, as well as the degree of contribution to cultural communication, is studied. The theme consistency score of the improved LDA model is -13.39 when the word generation probability is 1, which is 19.14% higher than that of the traditional LDA model. The intensity of the ‘cultural policy’ news format theme increases 14.44 times from 2010 to 2023, and the mean value of the ‘cultural governance’ news format theme’s contribution to cultural dissemination reaches 0.091. Based on the innovation and evolution of news forms, we can empower more communication channels for culture and spirit, so as to enhance people’s cultural self-confidence and national cultural soft power.

Dance drama is a comprehensive art with dramatic conflicts and plots based on the use of dance’s own language system, which plays an important role in cultural dissemination and aesthetic experience. The article designs a resource library of classic dance drama works in the way of WEB site, establishes a data dynamic distribution strategy to deal with structured data, and combines the consistent hash algorithm to optimize the load balancing of structured data in the resource library. Then, a graph convolutional neural network model and a sample-weighted aesthetic classification model are combined to establish an aesthetic assessment model for images of classical dance drama works, and a regularized matching module is designed. For the application effectiveness of the structured data processing strategy, the structured data processing of the classic dance and drama works resource library is verified, and the hyperparameters of the model, evaluation results and ablation experiments are also analyzed. Combined with the data in the resource library of classic dance drama works, the aesthetic experience of the audience was analyzed using a questionnaire. After using the dynamic distribution strategy to process the structured data, its write and query times were shortened by 40.05% and 17.89% compared to before use, and the response time under different index query load balance degrees did not exceed 55ms.The accuracy of the aesthetic assessment model for classical dance and drama works was 48.85%, and the accuracy improvement of the G-AANet model compared to BoTNet ranged from 0.93% ~ between 6.12%. The resource base of classical dance drama works established through structured data processing helps to enhance the audience’s aesthetic experience of dance drama works and helps them to appreciate the spiritual connotation of dance drama works.

This paper is based on the digital image processing technology, using the undamaged image information to restore and protect the frescoes. The discrete binary wavelet change is used to decompose and denoise the image signal. And decompose and filter the high-frequency component and low-frequency component of the image, choose different components, respectively, carry out coefficient transformation, and solve the OMP least-paradigm for different random matrices. The color space is selected, and the mural color space is channel decomposed according to the grayscale mode and restored separately. Establish an assumed datum for each independent face of the mural, establish a spatial coordinate system for it, realize the transformation of spatial coordinates, and realize the super-resolution three-dimensional reconstruction of the mural based on the generative adversarial network and the self-attention mechanism. Objective evaluation indexes and subjective evaluation indexes are established to compare the protection effect of different algorithms on murals. Compared with the traditional algorithm CDD, this paper’s algorithm improves the restoration time by 9.545~15.625 s, and the peak signal-to-noise ratio index improves by 1.35~4.769 db. In the results of the image extraction and processing, the calculated values of discrete curvature of the mural segments AB, CD, and EF ranges from -0.00945 to -0.00478, and the difference of standard deviation of the curvature from the target curvature is 6.477%. The approximate target curvature is obtained, and the algorithm has strong adaptive ability.

Electricity theft management is closely related to the economy of electric power enterprises. This paper proposes a power theft estimation method based on semi-supervised learning and time series analysis prediction. The electricity consumption data of power theft users are extracted as time series data, and in order to achieve multi-step prediction, MMD is utilized to improve the LSSVR semi-supervised learning algorithm. In addition, a perturbation term is introduced to optimize the convergence effect of the artificial bee colony algorithm, and a time series prediction algorithm based on improved artificial bee colony is established. Bringing in the power theft monitoring process to identify whether the user has power theft behavior, using the real power consumption dataset as the experimental validation data, comparing the identification accuracy of the prediction model. Predict the potential power theft of each user, solve the optimization model with the goal of optimal economic efficiency, and determine the actual ranking order of power theft users. The improved time series prediction algorithm proposed in this paper has a global error of 0.0003 and 0.0027 in dataset 1 and dataset 2, respectively, with the lowest global error and the highest overall accuracy of PSE prediction. And the algorithm predicts the list of users to be scheduled is basically the same as the list of users determined by the real PSE, which can achieve the maximum economic benefits.

Teaching evaluation is the feedback on the teaching effect of teachers and the learning effect of students. It has become a critical link in colleges and universities teaching management and teaching inspection. This paper proposes and applies an improved BT-SVM multi-classification algorithm to the education evaluation model. By calculating the relative distance between classes, the error accumulation phenomenon existing in the traditional SVM when dealing with multi-classification problems is solved. A classifier structure based on an incomplete binary tree is constructed to automatically classify teaching data by gradually dividing the data set and training the SVM classifier. By calculating the decision function value of the test sample in the binary tree, the category to which it belongs can be quickly determined. The education evaluation model follows the principle of legal compliance to improve the quality and efficiency of model evaluation and ensure the rule of law construction in colleges and universities. The research results show that the error rate of the BT-SVM algorithm in machine learning is below 0.1%, the fairness index is between 0.1-2, and the prediction accuracy is 96%. It shows that the machine learning algorithm can effectively improve the efficiency of education evaluation work and has the principle of fair legal compliance.

More and more problems are revealed in the process of popularization of higher education, especially the imperfection of the quality assurance system of higher education, which restricts and hinders the development of colleges and universities to a certain extent. This paper uses structural equation modeling to analyze the influencing factors of higher education quality. And consequently, it combines digital technology to build a higher education quality assurance system. Take a university as an example to practice, through the higher education quality assurance system evaluation index selection and empowerment, combined with the fuzzy comprehensive evaluation method to establish an evaluation model, to assess the effectiveness of the practice of the educational quality assurance system of the sample university. The management level (0.4380) and faculty (0.1472) of the university have the most significant influence on the quality of higher education. Under the constructed higher education quality assurance system, the comprehensive scoring result of the sample colleges and universities is at a good level (8.227), with the highest quality level in the dimensions of teaching effectiveness (8.7341) and student development (8.7000), which indicates that the digitization-based higher education assurance system is able to effectively ensure and promote the quality of education in colleges and universities.

Accompanied by the increasing consumer quality and the exploration of enterprises centered on user experience, the new retail model has emerged, and the emerging retail model also plays an important role in enhancing customer loyalty. This paper establishes an experiential sensory marketing model by combining perceptual theory and emerging technology from the basic features of the new retail model. Multiple linear regression model is used to study the influence of experiential sensory marketing mode on customer loyalty, and the correlation coefficient is used to analyze the correlation between the two. The correlation coefficients between the experiential sensory marketing model and customer loyalty range from 0.457 to 0.669, which is a moderate correlation. For every 1 percentage point increase in experiential sensory marketing mode, there is a significant increase of 0.647 percentage points in customer loyalty, and the average score value of customer attention under experiential sensory marketing strategy is 4.31 points. The sensory marketing strategy in experiential retail environment needs to improve the marketing standardization system, relying on professional service platform to improve the customer’s emotional experience, and then enhance customer loyalty.

Most areas in Hunan Province are rich in shale gas blocks, and their shale gas reservoir physical properties, geological characteristics, and enrichment rules need to be further studied. The article chooses the five # logging data of the Xiaoyanxi Formation in Anhua, Hunan Province, as the research object, preprocesses the logging data by curve environmental influence correction, curve reconstruction, and normalization, calculates the total organic carbon content and mineral composition change of shale gas by multiple linear regression, and uses the multi-mineral content calculated by optimization algorithm combined with the volumetric model to realize the matrix porosity of the variable skeleton. Then, the differential equivalent medium, self-compatible approximation, and K-T models were used to calculate the shale rock skeleton modulus. Then the shale gas reservoir petrophysical model was constructed. The adsorbed gas and free gas of the shale gas reservoir were solved separately to obtain the total gas content of the shale gas reservoir. The average TOC content solved by the model is 1.79%, which is only 2.23% higher than the absolute error of the actual data. When the volume fraction of the organic matter mixture increased from 0 to 0.25, the relative change of the longitudinal and transverse wave velocity ratio was only 0.87%. The shale gas content in Anhua Xiaoyanxi Formation 5# in Hunan Province ranges from 0.87 to 8.41 cm³/g, significantly higher than the lower limit value for shale gas industrial development. Recorded well data can clarify the reservoir’s physical characteristics of shale gas in Hunan Province and provide data support for exploring shale gas.

Supply chain optimization configuration contributes to the improvement and development of enterprise business application system. This paper takes the supply chain of manufacturing enterprises as the research object and analyzes the economic benefits of supply chain optimization of manufacturing enterprises. Aiming at the current development environment of enterprises, it puts forward the necessity of the development of enterprise supply chain flexibility, and establishes the overall supply chain flexibility model that contains the supply flexibility of the supply chain, the manufacturing flexibility and the distribution flexibility of the distributors. Simplify the total cost model of supply chain and establish the demand-driven supply chain optimization model. Analyze and validate the parameter settings of the improved particle swarm algorithm, and obtain the operating efficiency of the improved particle swarm algorithm with the changes of ordering cycle and inventory capacity. Combined with the sample enterprises, analyze the financial savings of each link after supply chain optimization. Further measurements show that after supply chain optimization of Company R, the saving percentage is 10.24%, and the annual saving amount is 562,807 yuan, with obvious economic benefits.

Whether tourism culture and economy develop in a coordinated manner is the key to realize the transformation and interaction of industrial structure. This paper takes the related data of 11 prefecture-level cities in Shanxi Province from 2013 to 2022 as the research object, and after demonstrating the intrinsic mechanism (the relationship of mutual influence) of the development of tourism culture and local economy, it applies the econometric panel Granger causality test to quantitatively test the interactive relationship between the development of tourism culture and local economy. After that, we constructed the index system of tourism culture and local economy, used entropy value method and coupling coordination model to analyze the comprehensive development level and coupling coordination degree of tourism culture system and regional economic system, and used Robust regression analysis to study the influencing factors of coupling coordination degree. The results of the study show that at the 5% significance level, with a lag of 5 and 6 periods, the local economic development is the Granger cause of tourism culture, and the local economic development has an obvious driving effect on tourism culture. In the 10 years of the examination period, the coupling coordination between tourism culture and local economy keeps growing, and the coupling coordination is improved, but there is still a certain gap with the high-quality coordination, meanwhile, the regression results show that focusing on the holistic and balanced development of the influencing factors is conducive to further coordination and interaction between the two systems.

The development of time and technology has made the traditional basic computer teaching unable to meet the needs of current students, and the cultivation of computer thinking ability has become a hotspot of computer education concern. The article combines the flipped classroom model with the MOOC platform of network education, and establishes a MOOC flipped classroom teaching model applicable to computer basic education courses in art colleges. In this model, students’ assessment results data are collected, and the Apriori algorithm optimised by matrix optimisation and prior pruning strategy is used to mine the association relationship of the assessment results, which helps teachers to understand students’ computer knowledge mastery. T College of Fine Arts is used as a research example to illustrate the effectiveness of MOOC flipped classroom through the changes in students’ performance, competence, and satisfaction. The improved Apriori algorithm has an execution time of only 2.93s when its minimum support is 100%, which can be used to understand students’ computer application ability for different question types, majors and skill performances. The mean score of the final exam of the students in the experimental class was 82.79, which reached 111.79% of the score of the control class, and more than 80% of them were satisfied with the MOOC flipped classroom. The use of flipped classroom and network education model can achieve the innovative development of computer basic education courses in art colleges and help to enhance the teaching quality of computer basic education courses in art colleges.

In the current period, green finance has become an inevitable trend in the development of the financial industry. The study collects the audience demand of green financial products through Octopus collector, uses micro-word cloud analysis system for data de-weighting and Chinese word segmentation, and calculates the keyword weights in the words using TF-IDF algorithm, and realizes the identification of green financial product innovation and economic benefits by combining with multi-dimensional innovation map. Subsequently, the indicators on green financial product innovation and environmental economy from 2007 to 2022 are combed, and a VAR-based econometric model is established to analyze the impact relationship between green financial product innovation and environmental economic benefits. The results show that when the lag period is 10 periods, the contribution of environmental economic benefit itself to the change of environmental economic benefit tends to be 23.79%, while the contribution of green investment products, green bond products and green insurance products to environmental economic benefit tends to be 9.72%, 20.23% and 21.83%, respectively. Green product innovation has a certain influence on the fluctuation of environmental economic benefits, and green bond products and green insurance products have a greater impact on environmental economic benefits.

This paper analyzes the development trend of Adobe After Effects-based movie special effects technology, including accelerated GPU research, integration and enhancement of 3D special effects production functions, cross-platform and cross-software collaboration, as well as the impact on the way special effects artists create. In terms of GPU research, the acceleration performance of different GPUs in movie simulation in China and abroad is compared, and the 3D effects production function involves the creation of 3D models, movie settings, rendering standardization, as well as the rendering output and the addition of special effects. Cross-platform and cross-software collaboration focuses on the cross-platform nature of AE, designing a tagged text file format and a movie playback engine based on the Cocos2d-JS game engine, and dividing the file system module. The analysis shows that the waiting time for movie rendering under this paper’s model is 1s and 2s, and the end-of-task ratio is 0.02, which are the lowest in both sets of experiments. The highest mean values for the 10 simulations of GPU utilization are 72.42% and 72.83%, respectively. It can be seen that the CPU acceleration model based on Adobe After Effect in this paper can effectively reduce the waiting time for movie rendering and improve the processing speed and stability of movie special effects.

This paper establishes a solution model for resource scheduling optimization in university laboratories, and sets the corresponding constraints and objective functions. The genetic algorithm under the heuristic algorithm is used to solve the resource scheduling optimization problem. On this basis, the pyramid model is constructed, the population evolution and variant strategy are proposed respectively, the model genes are labeled with scheduling cost adaptation, and the genes are generated in series. The framework of scheduling algorithm is proposed, and the dynamic scheduler is constructed to realize the scheduling of university laboratory resources. Through simulation experiments and algorithm analysis, the effectiveness of the use of the model is verified. The experimental results show that when the number of simulation is 10 times, the fitness of the population is 20, 100 and 200 respectively. After the implementation of scheduling for college laboratory resources, the utilization rate of laboratory equipment is increased by 16.3%, 34.6% and 18.4% respectively.

The aim of this paper is to improve the advertisement display effect and realize accurate placement in the market. Firstly, the convolutional neural network is used to select the advertisement keywords, and optimize the click rate, conversion rate and so on when the number of iterations reaches a certain value. Next, the established hierarchical analysis model is used to conduct a comprehensive evaluation of online advertisement release forms, and select the advertisement form that best suits the needs of the enterprise and the market environment. The weight of the webpage and the similarity between the center of mass of the webpage and the advertisement are used to calculate the final score, and the advertisements are sorted to achieve the improvement of the display effect and placement accuracy of the advertisements. The final analysis found that for short-term user behavior, the weight of text link ad clusters is as high as 0.66, which can improve the accuracy of ad placement. For long-term user behavior, the multi-objective optimization algorithm can accurately identify and assign high weights when users continue to visit specific web pages, for example, the cluster of web banner ads reaches 0.64. Meanwhile, it can be adapted to different application scenarios, and the weight of text link ads cluster is significantly increased from 0.14 to 0.758 when the freshness factor is increased from 0 to 1. The optimal F1 value of the advertisement delivery effect is 97.24, which is the highest F1 value of AIGC. The AIGC ad placement strategy provides a new method for the intelligent development of the advertising industry.

In order to study the role of digital economy on the transformation of regional economic structure, firstly, the mechanism of the role of datatized economy on the change of regional economic form is elaborated, and on the basis of the analysis of theoretical model, the structure of the distribution of capital factors in each industry and the ideal factor are determined. Determine the index system and weights of regional economic structure transformation through the selection of weight indicators, and complete the measurement of the data-based economic situation under the construction of the index system of data-based economic situation. Two hypotheses are proposed that digital industrialization can have an ideal effect on the structural transformation of local development but the shape of the effect is inverted U-shape, and that industrial digitalization can have an ideal effect on the structural transformation of local development. The empirical analysis finds that the Moran’s I index of structural transformation of local development from 2008 to 2020 is prominent in the 1% case, and the FP and UE within, central, eastern, western regions of China and the level of structural transformation of local economy is prominent in the 1% case. It is concluded that there is a prominent spatial isotropic relationship between the datadriven economy on regional economic structural change resilience in the whole region, and the constructed research model has a good robustness.

With the development of Internet of Things (IoT) technology, improving the interactivity of IoT communication teaching has become an important research content. This paper firstly constructs the IOT communication teaching system on the basis of service layer, network layer and teaching layer, through which the teaching information is ensured to be delivered timely and accurately. Secondly, the group intelligence algorithm teaching interactivity is optimized and designed to optimize the teaching environment, network, and teaching layer to get the optimized server resource allocation scheme to achieve the optimization of different levels in the teaching of Internet of Things communication. When the number of iterations reaches 20 and 45, the adaptability of this paper’s algorithm is maintained between 100-10-1, and the optimization of the algorithm improves the student participation, the depth of understanding of knowledge, the accuracy of data, the speed of transmission, the efficiency of management, and the teaching effect by 28.6%, 41.7%, 4%, 100%, 18.8%, and 20%, respectively. In the delay analysis, when the number of terminals is 10, 20, and 30 respectively, the delay of the teaching system in this paper is the lowest among all the compared systems, which is 10ms, 40ms, and 230ms respectively.This study can lay the foundation for improving the quality and effect of IoT communication teaching and promote the cultivation of teaching interactivity between teachers and students.

In order to satisfy consumers’ needs, enterprises must conduct in-depth research on consumers’ purchasing behaviours and design and develop marketing strategies based on the characteristics of consumers’ needs. The article takes 4P marketing theory and SOR model as the guide, and establishes a consumer purchase intention model in combination with the consumer behaviour model. The questionnaire is designed from the product value, price range, channel optimisation, and promotional efforts of the enterprise marketing strategy, and the validity of the questionnaire is tested by principal component analysis. Then meta-analysis method was used to explore the correlation of each variable, and the SEM model was combined to explore the influence path of corporate marketing strategy on consumer purchase intention. The Q-value of the hypothesised relationship of consumer purchase intention ranges from 446.137 to 814.535 and is significant at 1% level, and the correlation coefficients of each variable in the model with consumer purchase intention are more than 0.35. The indicators of model fit, CMIN/DF and RMSEA, are 1.076 and 0.015 respectively, and the path coefficient of the value of the product in the marketing strategy on the purchase intention is the largest at 0.076. The path coefficient of product value on consumers’ purchase intention in marketing strategy is 0.369, and the development of enterprise marketing strategy needs to actively expand marketing channels and design differentiated product and service programmes, so as to enhance consumers’ recognition of the enterprise brand to stimulate their purchase intention.

With the proposal of sustainable development of energy, countries begin to develop from fuel vehicles to new energy vehicle market. Firstly, we construct a consumer purchase behavior recognition model based on XG Boost algorithm, simulate the gradient enhancement process of purchase behavior recognition, obtain the approximation value based on function calculation to become the learning target of the overflow value, and at the same time, give higher learning weight to the samples with unsatisfactory accuracy in the last round, and after continuous iteration, gradually correct the purchase behavior recognition bias. According to the number of purchase behavior features identified correctly, the number of features that do not have purchase behavior features, and the number of features that are not identified, invalid users are eliminated to improve the accuracy of the algorithm. The Cronbach’s alpha coefficients of the four factors are found to be 0.891, 0.895, 0.813, and 0.800, all of which are greater than or equal to 0.800, indicating that the factors are internally consistent. And the relationship values between the factors and purchase intention are 0.439, 0.406, 0.430, 0.387, which are all greater than 0. Therefore, there is a prominent relationship between all four dimensions of consumer purchase behavior factors and consumption impulse, and the identification of purchase behavior patterns has a guiding role in electric energy vehicle marketing strategy.

In order to be able to accurately identify user behavior and emotional tendency, this paper firstly adopts the neural network structure to build the emotion analysis model, and divides the model into four parts to analyze the text and emotion in social media, and obtains the information of semantics and emotion-related content in social media text. Secondly, from the semantic and emotional symbol content of the text in social media, the public emotional tendency model is built, and the sharing content and behavior of a large number of users in social media are analyzed. Finally, the association rule mining algorithm is used to extract the text and emotional symbols in social media, to improve the accuracy of the user’s emotional tendency analysis model, and to be able to accurately derive the user’s behavioral habits. In order to verify the analytical effect of the model, the model was tested, and the training speed of the BLSTM model was fast, and the training time was 1.5 hours in the first iteration of the test with a data set of 1 million. The model is more accurate in analyzing the user’s positive emotions, with accuracy and precision around 85% and 90% respectively, and the results obtained are more accurate, meet the user’s needs, and enhance the user’s experience.

Tang Dynasty costumes are regarded as a brilliant brushstroke in the history and culture of Chinese costumes, and the fate of the whole Tang Dynasty can be analyzed through the evolution of Tang Dynasty costumes. In this paper, we have constructed a dress semiotics system from the social level, psychological level and cultural trait level, through the transfer of the imagery dress structure to the real dress, to express its symbolic meaning, and applied the constructed system to the Tang dress symbols, to interpret the meaning of the Tang dress symbols from the two levels of society and culture. Using CiteSpace information visualization software, combined with the literature of “Tang Dynasty Costume”, the study explores the dynamic evolution law of Tang Dynasty costume culture. The results of the study show that the earliest year for the keyword “Tang Dynasty costumes” is 1985, and the frequency is as high as 68 times. The keywords with the highest degree of centrality are dress and Tang Dynasty culture, both of which are 0.38. A number of new keywords with strong salience emerged in 2013-2023, among which the ones with a sudden increase of intensity greater than 5 are artistic features, clothing styles, clothing colors, clothing shapes, sweater, structural design, cheongsam, and knitted fabrics, and therefore the future hotspot of the Tang Dynasty dress research shifts to these keywords.

In order to be able to study in-depth image recognition technology for the detection of emergencies, this paper firstly adopts the image processing technology image processing, removes the noise in the image, improves the clarity of the image, and reduces the distortion of the image. Secondly, the signal in the image is extracted, and the network transmission algorithm is used to detect the signal in the image and calculate the corresponding transmission energy value. Finally, a standard threshold is set according to the calculation results, and once the transmission energy exceeds the threshold, it is an abnormal event. The analysis of the emergency event detection model based on image recognition technology shows that the image contrast effect is good, around 8.5 points, indicating that the image quality obtained based on image recognition technology is good. For the third emergency detection, the value based on image recognition technology is 93.3%, the detection results are more accurate, the response speed is faster, the fastest can reach about 1.1s, can real-time feedback on the results of the detection of the emergency situation in a timely manner to deal with the emergency situation, to reduce the loss of personnel, and to improve the efficiency of the management of the smart community emergencies of public health events.

The integration of modern information technology and civil litigation promotes the electronic civil litigation, online litigation as a kind of litigation behavior, promoting the development of traditional trial mode. This paper starts from analyzing the relationship and conflict between civil e-litigation and traditional civil litigation, and organizes the relationship between online trial mode and traditional court trial, and the relationship between civil online trial mode and traditional trial mode respectively. Based on the influencing factors of civil trial, the time proof consensus algorithm and data security transmission algorithm are respectively proposed to combine the network nature of online litigation and blockchain storage data information to optimize the online litigation electronic evidence storage. Summarizing the litigation efficiency of online trial and traditional trial under different control variables, from the point of view of the complexity of the case, the litigation efficiency of online trial mode for more complex cases is significantly higher than that of traditional trial mode. For non-complex cases, the efficiency increases but the difference is not significant. The online litigation mode is a part of the civil online trial mode and serves the traditional civil trial mode.

In order to accurately assess the financial status of a company and identify potential anomalies, this paper first implements unsupervised classification of financial transaction data based on Support Vector Machines, which automatically classifies the data into normal and abnormal categories. Histograms are introduced in combination with LightGBM to quickly fuse data from multiple sources. The most suitable first layer is selected by different algorithms, and the outputs of these algorithms are combined with industry-wide common abnormal features as inputs for LightGBM’s second layer identification. With this two-layer structure, the model not only takes into account the industry characteristics, but also the common anomaly features. Empirical results show that in the accuracy of smart financial statement generation, the sensitivity of this paper’s model iterates to 99.99% at 41.25% specificity, and the accuracy of this paper’s model is as high as 0.98 when dealing with financial private information, macroeconomic, and market information.In the identification of financial transaction anomalies, the number of anomalous weeks is identified to be 24, 29, 34, and 36, and the fusion of multi-source data effectively identifies the large amount of financial transactions, fluctuating transactions and other suspicious abnormal transactions.

This paper examines the differences and convergence of regional real estate markets based on panel statistics of 28 provinces, autonomous regions and municipalities directly under the central government in China from 2010 to 2023. Relevant variables such as urban construction land area, population and economic growth are set and the data are processed. The data show that the degree of industrial convergence and circulation costs have a positive spatial correlation and an upward trend from 2015 to 2021. From the viewpoint of regional real estate market divergence, the proportion of the real estate industry in GDP has remained above 5% since 2015, and this proportion is larger in the eastern region, for example, it was 8.74% in Beijing in 2015, but it has slightly decreased in some provinces and cities. The proportion in central and western provinces and cities has been rising faster year by year. The extreme deviation and standard deviation coefficient of the eastern region are relatively large, with the extreme deviation of the eastern region being 4.35% and the standard deviation coefficient being 1.45529 in 2021, indicating that the internal development is not balanced. From the analysis of convergence, the rate of convergence in the absolute convergence test is 3.66%, and the rate of convergence in the conditional convergence test is 2.89%, with a half-life of about 23.8 years. It indicates that the regional real estate market differences are shrinking, showing a trend of convergence, but the convergence process is relatively slow, which provides an important basis for an in-depth understanding of the characteristics of the regional real estate market.

With the increasing complexity of the financial market, corporate financial fraud events occur frequently, posing a serious challenge to investors and market regulators. Aiming at the limitations of traditional financial fraud recognition methods, this paper constructs a financial fraud recognition model MCN based on the topological data analysis method. The model consists of two parts: the Mapper algorithm and one-dimensional convolutional neural network (1DCNN), which combines the global topology extracted by the Mapper algorithm with the local features of the IDCNN to realize the effective identification of financial fraud samples. In order to evaluate the recognition performance of the model, this paper controls the topological feature extraction method unchanged and the classifier unchanged respectively, and compares the performance of the MCN model with other financial fraud recognition models. The results show that the Acc and F1-score of the MCN-based financial fraud recognition model in this paper are 98.69% and 97.64%, respectively, which are better than other models in both perspectives, proving the superiority of the financial fraud recognition model based on topological data analysis constructed in this paper, and thus providing powerful technical support for the regulation of the financial market and the risk management of enterprises.

In this paper, the image parameters are preprocessed by the gray scale histogram statistical image parameters, which reflect the gray scale distribution information of the plant images, using the zero-mean normalization formula. According to different lighting conditions, the plant image is segmented, and the texture feature information in the plant image is extracted by using the improved grayscale covariance matrix. The hyperspectral linear mixing model is constructed, and the MVSA algorithm meta-decomposes the mixing model to solve the solution optimization problem. Using the natural gravity embossing method, produce plant embossed flowers and analyze the features and spectral curves of different parts of the embossed flowers to evaluate the comprehensive use of the embossing method proposed in this paper. The ROI images of 1200 embossed pattern petals were calculated to obtain the sample spectral matrix of embossed petals, in which the reflectance of the central petal was the highest among the three parts at a wavelength of 450 nm, with a reflectance of 0.46487, and then decreased, and then gradually increased to one place after the wavelength was equal to 694, with a reflectance of 0.8. The reflectance of the Shaanxi Weixiang (Weixia), the single side-embossed Yuanbaosi (Yuanbao maple), the hammered elm (fruits), and the pachypodium (Green) obtained a full score of 35 in the comprehensive evaluation after drying, which is a perfect embossed plant material, and all the plant materials embossed using the method proposed in this paper averaged above 30, and the comprehensive effect of plant embossing was good.

Since the financial crisis, the economies of all countries have been affected by the recession triggered by global events, and the uncertainty brought by the changes in economic policies has also become a risky shock, and the uncertainty of economic policies has been climbing worldwide. This paper firstly briefly analyzes the mechanism of economic policy and financial market, in order to comprehensively study the changes of market economic liquidity, this paper starts from the return of the market economy, and adopts the symbolic time series analysis method to analyze the prediction of the financial market by taking the stock market as an example. Then construct the regression model, and then study the impact of economic policy uncertainty on market liquidity. The regression coefficient of economic policy uncertainty is 0.064, which is significant at 1% level. Secondly, when GDP growth rate and inflation level are added as control variables, the regression coefficient of economic policy uncertainty obtained is 0.108, which is still significant at 1% level, implying that a rise in economic policy uncertainty brings about a decline in market liquidity. This study provides an effective analytical tool for the impact of economic policies on market liquidity. It also provides a basis for the government to improve market liquidity and enhance market vitality.

The study adopts a detection followed by tracking paradigm. In the detection stage, the BiFormer dynamic sparse attention module is embedded in the YOLOv8 network model, while the original nearest neighbor interpolation upsampling is improved by replacing it with the lightweight upsampling operator CARAFE. In the target tracking stage, a multi-vehicle steering trajectory tracking algorithm based on particle filtering is proposed, and the particle filtering algorithm is improved by combining the target motion direction weighted resampling algorithm. The two improved algorithms are combined for multi-vehicle detection and tracking in tunnel scenarios, and the average tracking accuracy can reach 97.3%. Compared with the traditional YOLOv8 combined with particle filtering algorithm for tracking, the method in this paper is more advantageous.

Tunnel gas and shallow natural gas overflow have been a major problem plaguing the safe construction of tunnels and one of the main types of common diseases in tunnel engineering. The article chooses the tunnel construction of Funci Highway as the research object, and collects the rock and gas data in the research area on the basis of analyzing the distribution characteristics of shallow natural gas. Based on the AVO analysis technique, the PP wave reflection coefficient is approximated as a linear combination of longitudinal wave velocity, transverse wave velocity, density and other elastic constants to construct a pre-stack AVO inversion model to analyze the shallow natural gas distribution in the Funci Highway Tunnel construction. The porosity of the rock layer in the tunnel construction area ranges from 4.5% to 12%, with an average porosity of 8.93% and a maximum permeability of 0.004 μm². The longitudinal wave impedance distribution of the non-reservoir surrounding rock ranges from 1.48 to 2.01, and the error between the longitudinal wave velocity and density obtained by the inversion and the original logging curves is up to only 2.04%. Combined with the logging data, it can realize the comprehensive evaluation of the oil and gas geological environment of Funci Highway tunnel construction, and provide data support for ensuring the safety of Funci Highway tunnel construction.

This paper applies smart technologies to urban rain garden design and analyzes the hydrological effects based on urban smart rain garden technologies. The SWMM model is used to simulate runoff water quantity and quality under the environment of long-term and continuous rainfall events in urban areas. Building area A is selected as the case study object of this paper, and its geographic location and precipitation data are analyzed to preliminarily explain the hydrological conditions of the case study area. Based on the SWMM model, the model pipe network generalization and other operations are carried out to establish the SWMM model of the study area. The SWMM model is calibrated in terms of the calibration of the model’s parameters and the feasibility of the structured network SWMM model to verify the validity of the SWMM model of the study area and its catchment delineation method. Based on the urban smart rain garden technology, the LID module is added to the SWMM model of the study area and the hydrological effects under different scenarios such as combined LID are analyzed. Each LID measure can have a certain reduction effect on the combined runoff coefficient and total runoff. The combined LID measures in this paper have the best reduction effect, with the reduction rate of the integrated runoff coefficient over 35% and the total runoff over 50% in the 2h rainfall event. The combined LID scheme has the best reduction effect on the flood peak, and the reduction rate can reach more than 40% in both 2h rainfall events.

Curriculum Civics refers to the integration of Civics elements into the teaching of professional courses, so that courses other than Civics courses can also play the role of Civics teaching. In this paper, we study a knowledge mapping-based content generation technology for teaching course Civics and Politics, so that the knowledge of Civics and Politics courses can be integrated and visualized. The knowledge points, concepts, definitions and other information of the course Civics and Politics are extracted in the form of Civics and Politics knowledge triples. Through the extraction of the knowledge entity of curriculum Civics and politics, the relationship between semi-structured data and unstructured data is extracted to realize the integration of knowledge and content generation. After achieving content generation, the generated content is personalized through a deep reinforcement learning recommendation algorithm based on diversity optimization. Taking the two courses of Engineering Cost Management and Engineering Economics in the engineering management specialty as an example, it is found that the proposed knowledge graph construction method has an accuracy rate of 96.2%, which is able to effectively establish the knowledge association between the civic elements and the elements of professional knowledge, and realize the mining and generation of the civic elements. Meanwhile, the DDRL-Base recommendation algorithm achieves the optimum in accuracy, recall and F1 value indexes, and optimizes the problems such as cold start and sparse data in resource matrix, which improves the effect of recommending the Civics and Politics teaching content of the course.

The technical analysis of conventional tennis sports basically focuses on individual studies, with less research on the basic theory of tennis, and the theoretical analysis of tennis trajectory is even rarer. In this study, based on the calculation equations of the main forces during tennis movement, the dynamics analysis of tennis serve movement is carried out, and the three-dimensional trajectory equations of tennis serve are established. Then, based on the ODE dynamics engine technology, the simulation platform of tennis serve is built to realize the simulation and visualization analysis of tennis trajectory. Since the simulation system beat frequency is 1000Hz, the time difference between tennis simulation and actual movement is the smallest, so the frequency of 1000Hz is chosen for the simulation study of tennis serve trajectory. The simulation results show that under the same hitting height and ball angle, the larger the initial velocity of the tennis ball is, the farther the X-axis landing point is from the center line. In addition, under the consideration of air resistance and Malnus force, the difference between the Y-axis landing point of tennis ball when the initial serve angle is 30° and 60° is 1.81098 m. The present study provides a certain reference for the in-depth study of the serving strategy of tennis ball, and at the same time, it also provides a certain theoretical basis for the improvement of the tennis players’ training method and technical playing style.

The temperature gradient formed by cooling and heat dissipation after shutdown of aero-engine will lead to thermal bending of rotor, which is also the main reason for bending vibration of rotor after engine secondary start-up. In this paper, the rotor system model of a GTF motor considering the gearbox structure is established, and the thermal bending deformation of the fan-gearbox-low pressure rotor caused by temperature gradient is analyzed. The critical speed of the rotor system considering the temperature field is calculated and the vibration characteristics of the engine after the second start are analyzed, which provides a reference for the design of the rotor system of GTF engine. The results show that the rotor mainly appears hot bending deformation in the direction of vertical axis, especially in the joint of disc axis. The large bearing stiffness of the gearbox has obvious inhibition on the hot bending deformation of the rotor, and the effect is obvious when the bearing stiffness is above 1E6. The vibration characteristics of rotor are greatly affected by temperature field. The amplitude of rotor system is larger and the sensitivity of gearbox structure is higher under the influence of temperature field. The amplitude is also the largest when the thermal bending amount is maximum about 10min, and the amplitude decreases by 50% after 40min. The bearing stiffness of the gearbox has a great influence on the vibration characteristics of the rotor system with hot bending deformation, and the vibration suppression effect is best when the bearing stiffness is between 1E6 and 1E8, and the peak point above 1E8 is close to the operating speed of the fan, which is bad for the safe operation of the engine.

Sustainable agricultural development is a key component of the rural revitalization strategy, and strengthening the guidance and support for sustainable agricultural development is an inevitable choice for improving agricultural production capacity and realizing rural revitalization. The study constructs an evaluation system of sustainable agricultural development based on four dimensions: economic opportunity, social well-being, environmental quality, and climate action, selects relevant index data of each province from 2004 to 2022, and adopts a multi-level factor analysis method to comprehensively evaluate the sustainable agricultural development as well as the dynamic distribution of 31 provinces in China. The results show that Henan leads other provinces in economic opportunities with a score of 1.21, and Hebei ranks first in social well-being with abundant human resources and policy support. In the level of regional sustainable agricultural development, there is an uneven distribution pattern of “North > Central > South”. From the dynamic distribution of agricultural sustainable development in 31 provinces from 2004 to 2022, it is concluded that the development trend of agricultural sustainable development in China is better, and the gap between provinces has been narrowed. Finally, policy recommendations are put forward based on the situation of agricultural sustainable development to provide reference for the subsequent work on agricultural sustainable development.

The integration of Civics elements into the EFL classroom is an organic supplement and deepening of the teaching content and materials, while EFL Civics classroom teaching is a powerful means to strengthen the deep and longitudinal development of students’ critical thinking. This paper discusses the relationship between EFL Civics classroom teaching and the development of critical thinking ability from the theoretical and practical levels respectively. On the basis of existing research, the evaluation index of students’ critical thinking ability is proposed. The CVM coefficient of variation method is improved, and the ICVM and BP neural network algorithm are combined to constitute the evaluation model of students’ critical thinking ability based on ICVM and BP neural network. According to the evaluation process, the level of students’ critical thinking ability after EFL-based Civics classroom teaching is derived. It also integrates teachers’ and students’ evaluation of the effect of English Civics elements integrated into the EFL classroom, and finally obtains the practical teaching effect of the EFL Civics classroom. The overall mean value in the teacher’s side is greater than 3.5 points, which indicates that teachers are basically positive about the effect of integrating Civics in EFL courses, and basically agree with the positive impact of English Civics elements on EFL classroom teaching. Based on the evaluation results of ICVM and BP model, the evaluation scores of students’ critical thinking skills and critical thinking monitoring are higher than the evaluation scores of critical thinking tendency, i.e., the elements of English Thinking can be effectively integrated into the EFL classroom and promote the development of students’ critical thinking skills.

Digital teaching strategies can significantly stimulate students’ interest in learning and provide personalized learning pathways. This paper proposes a multimodal action recognition method that integrates the word vector method, and designs a teaching decision optimization strategy based on this idea. Firstly, we compare the information of different modalities, complete the construction of multimodal action recognition network through the processing of image information and optical flow information, and combine the word vector method to guide the semantic learning of students’ actions. Then the design and realization process of the teaching decision aid system is introduced. Based on the above proposed action recognition method to collect students’ classroom behavior data for model training to be used in the system, the system consists of four modules: model training, classroom data collection, behavior recognition and data presentation. After the data collection, the action recognition of student behavior is carried out to provide teachers with feedback on student behavior information and assist them in making teaching decisions. In this paper, the above algorithms and systems have been verified by relevant experiments. After comparison with other algorithms, it is verified that the multimodal action recognition method designed in this paper, which incorporates the word vector method, has a high accuracy rate. In the comparison of the overall quality of instructional design decisions, the average value of the instructional decision aid system in this paper is 17.35, which is higher than the average score of excellent human teachers in the overall quality of instructional design decisions, indicating that the instructional decision aid system designed in this paper achieves the optimization of instructional decisions and reaches the level of excellent decisions.