Dress metaphor is a very important way of expression in the novel text of Ming Dynasty, and the recognition and interpretation of the metaphor play a very important role in really understanding the novel text. This paper proposes a dress metaphor recognition model based on Transformer and graph convolutional neural network, and a dress metaphor interpretation method based on Seq2seq framework. The apparel metaphor recognition model performs feature extraction of global and local information of apparel metaphor sentences by Transformer. Graph Convolutional Neural Network is utilized to obtain syntactic structure information and sentence dependencies, in order to complete multi-word dress metaphor recognition. Then the obtained deep metaphor features and syntactic structure information of the sentence are input to the classification layer. The metaphor decoding method carries out costume metaphor understanding through the encoder-decoder, which chooses the LSTM network structure for both encoder and decoder to better obtain the semantic features of the novel text. The dress metaphor recognition model improved the recognition correctness on the dataset by 17.97% and 7.28%. The dress metaphor interpretation method based on the Seq2seq framework elaborates the interpretation content and can more accurately interpret the dress metaphors in Ming Dynasty novels. It verifies the practicality of the metaphor recognition and interpretation model in this paper in the task of interpreting dress metaphors in Ming Dynasty novel texts.

The higher the corporate financial transparency, the more it can reduce the information asymmetry, which can enhance the market trust and improve the corporate performance. In order to improve corporate financial transparency, the study constructs a financial fraud identification model by improving the machine learning model based on XG Boost algorithm from the financial fraud factors. Based on the XG Boost algorithm, the model integrates the decision rules through the weighted fusion method to generate a new decision tree to determine the financial fraud. In order to improve the ability of enterprise performance assessment, the baryon support vector machine method is used to classify the performance of enterprise employees, and the nonlinear baryon support vector machine is used to establish the enterprise performance assessment model. In the process of verifying the effect of the two models, text indicators are extracted using big data technology to provide a rich feature set for the financial fraud identification model. The data from ERP, CRM and other systems are integrated to provide a comprehensive and high-quality data set for the enterprise performance assessment model. After empirical analysis, the combination of big data and machine learning can improve the effect of financial fraud identification, and then effectively improve the transparency of corporate finance. The enterprise performance evaluation model provides a scientific and efficient quantitative evaluation tool for enterprise managers, and effectively improves the enterprise performance evaluation capability.

The integration and development of Sichuan’s rural music and cultural tourism industry is of great signiϐicance in the context of rural revitalization strategy. The purpose of this paper is to construct a multilevel regression model to deeply explore the inϐluencing factors and role mechanisms of the integration of the two. Through theoretical analysis and empirical research, the research variables are clariϐied, and the null model, random effect model and complete model are constructed and data validation and analysis are carried out. The results show that the richness of rural music resources, the level of cultural and tourism industry, policy guidance and support, market demand and human resources have a signiϐicant positive impact on the integration of rural music and cultural and tourism industry in Sichuan. The results of the full multilevel regression model show that the same level of rural music resource abundance has different impacts on the integration of rural music and cultural and tourism industries due to regional differences. The results of the study provide theoretical support for the development of cultural tourism industry in Sichuan Province, and deeply help the implementation of rural revitalization strategy in Sichuan Province.

In order to improve the accuracy and efficiency of medical image segmentation, this paper designs and proposes a medical image visualization method containing Sobel edge detection operator and 3D UNet network based on deep learning and edge detection. The 3D U-Net network is used to capture the morphological and edge features of medical images on the public dataset, and the image binarization is performed on the result of its operation. The binarized image processed by corrosion and expansion algorithms is multiplied by the corresponding elements of the matrix with the medical image to obtain the visualization of the medical image. Different comparison algorithms and data sets are selected to verify the effectiveness of the optimized 3D U-Net network module and feature fusion module. Parameter settings are carried out, and the LIDC-IDRI dataset is used as the algorithm training base data to analyze the segmentation accuracy of the image processing method that fuses the edge detection operator with the 3D U-Net network. The algorithm ablation experiments are carried out according to different pruning degrees and training methods. The algorithm in this paper can achieve more than 80% segmentation accuracy on LIDC-IDRI dataset, in which the segmentation accuracy of liver reaches 97.1%.

In order to improve the teaching effect of dynamic structural behavior simulation in structural engineering teaching, this study develops a dynamic structural behavior simulation teaching model combined with the finite element method to explore the effect of its application in teaching. This paper first introduces the process of applying the finite element method to simulation teaching and the steps of structural engineering system development. After that, it introduces the common structural engineering analysis functions under ANSYS software and its application in various aspects of structural engineering teaching. Then the construction process of the dynamic structural behavior simulation teaching model is briefly described, and the finite element principle is combined with the actual engineering problems through the integration of case teaching to realize the deep integration of theory and practice. Finally, the teaching model of dynamic structural behavior simulation is constructed and the teaching evaluation system after applying the model. The results of teaching practice show that more than 95% of the students maintain a positive attitude towards the use of the model in this paper. Under the teaching mode of the simulation model visualizing dynamic behavioral characteristics, the average grade of students in the experimental group was significantly higher than that of the control group by 14.96 points, and the difference between the grades of students in the two classes was significant (P=0.000). It can be seen that the use of the model can improve the students’ understanding of dynamic structural mechanical behavior and the application of finite element analysis tools, which provides an efficient platform for combining theory and practice for structural engineering teaching.

Following the footsteps of the times, an excellent and complete movie cannot be separated from the application of digital modeling. In this paper, we mainly use 3D modeling, motion capture, rendering and other related technologies to edit and produce the character’s physique, proportion, contour, etc., design the character’s expression, color and action, and build the film and television scenes in 3D space. Thus, it realizes the characterization and emotional expression in film and television. Will be through the traditional 2D film and television and three-dimensional film and television control experiments, from the experimental data can be seen, in the frame rate, three-dimensional modeling technology film and television than the traditional 2D film and television on average 14% to 20% higher. There is also a leading edge in the number of textures. The data color emotion analysis indicated that the color shift and strong contrast connects the plot and the audience’s feelings. The quantitative survey of emotional experience through questionnaires shows that the audience in the 3D film and television group is higher than the traditional 2D film and television in terms of immersion experience, interaction experience and learning and enjoyment experience. Therefore, 3D modeling technology plays an important role in the creation of film and television art.

The study proposes a dynamic resource allocation model suitable for English language teaching, which combines learner characteristics, learning progress and resource availability to achieve real-time optimal allocation of resources through mathematical optimization algorithms. A multi-objective optimization model is constructed based on the key factors in resource allocation for English teaching. Facing the optimization objectives of maximizing learning efficiency and minimizing resource idleness, NSGA-II algorithm is used to construct a non-dominated solution to achieve global sorting, and combined with congestion calculation to complete global quality population screening. At the same time, the branch delimitation algorithm is utilized for local search of optimal solutions, and merged with the population of NSGA-II to generate the new generation of optimal populations. The optimization probability of the combined algorithm in this paper is 0.85, and the average convergence error is only 0.01081, which has excellent optimization performance. The resource allocation delay of this algorithm is around 0.1ms, and the allocation efficiency is more than 95%, and the comprehensive effectiveness is better than the comparison algorithm. The dynamic allocation model of resources in this paper improves the balance of resource allocation of English teaching and auxiliary room area, the number of teaching materials, the number of full-time teachers and teaching equipment. At the same time, it prompted the average English score of the experimental class to exceed 80, which was significantly higher than that of the control class.

How to give full play to the clarinet in the symphony orchestra in the sound advantages and characteristics of the role, undoubtedly is an important topic of the current music research. Combined with years of working practice and learning experience in the symphony orchestra, the author explains the tonal advantages and characteristics of the clarinet in the symphony orchestra. For the study of the relationship between its tonal advantages and characteristics and the symphonic concerto, the author combines the finite element method in the music education environment, through the method of computational simulation, to explore the symphonic performance conditions, as well as the main discussion on the analysis of the boundary conditions with the vibration velocity and sound-absorbing materials, in order to achieve the purpose of improving the clarinet’s musical and artistic level in the symphony orchestra. Through the study, we found that the numerical simulation of the relationship between the clarinet technology and the symphony orchestra concerto is analyzed by the local fundamental solution method with high computational accuracy, which lays the foundation for the successful application of this method to the numerical simulation of the sound field of the complex music education environment.

Cheerleading events are flourishing in China, the level of competition is rising, the number of competition groups and programs is increasing, the competition is becoming more and more intense, and the innovative research on formation design is an inevitable demand for the development trend of cheerleading. The study designed a multi-objective path planning model based on the intensity of willingness and consultation strategy, so that college cheerleading can avoid conflicts and reach the goal point of cheerleaders in the complex environment. Then an improved multi objective particle swarm algorithm (MOPSO-CA) based on meta cellular automata is proposed and applied to college cheerleading formations to realize the design of college cheerleading formations. The simulation results show that the MOPSO-CA algorithm can re-select the optimal movement direction angle according to the real-time positions of the moving obstacles and moving targets, which illustrates the effectiveness of the algorithm. Secondly the feasibility of the formation design conditions are suggested as: keeping the originality of the movement, the use of the moving route of the formation and the space of the venue, and the type of formation change.

The article solves problems such as personalized investment, and then achieves the expected effect of investment decision-making. The article firstly designs an investment decision support model based on collaborative filtering, elaborates the implementation path to realize investment decision support from the perspective of machine learning, and then combines the user image technology to design the user image labeling system and model construction. Finally, the effectiveness and rationality of the proposed method in this paper are verified through experiments. Experiments on a corporate investment decision support task on a company’s dataset reveal that the method proposed in this paper has good performance on all metrics, with the highest value of 0.6985 on AUC.This gives an indication of the effectiveness of the financial data analysis and investment decision support model proposed in this paper.