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.

In today’s era, the continuous development of artificial intelligence technology has brought great changes and impacts on the field of warehousing and logistics, which improves the efficiency of the traditional warehousing and logistics field and reduces labor costs. In this paper, firstly, the least squares support vector machine is used to establish the inventory prediction model of warehousing logistics, and the value of inventory in warehousing logistics is predicted according to the model. Secondly, the logistics and warehousing automation architecture is designed to utilize the interaction between different layers to complete the transfer of data and feedback of information. Finally, the established architecture is utilized to form a detailed inventory management model for warehousing and logistics, so as to manage the inventory in logistics and warehousing. The analysis of the inventory prediction and management strategy of artificial intelligence technology shows that the value of artificial intelligence technology is 229, and the actual value of inventory is 230, which indicates that the prediction effect of artificial intelligence technology is better, and the prediction result is more accurate. The analysis of the turnover efficiency of artificial intelligence technology can be concluded that the inventory turnover rate of artificial intelligence is 5 times/month, the turnover efficiency is higher, which can reduce the backlog of inventory, so that the management efficiency of inventory can be improved, and the artificial intelligence technology can accurately predict the value of inventory, and improve the satisfaction of users.

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.