Social network is a special social factor in the development of cooperatives, and the influence of the degree of social network embeddedness cannot be ignored in order to realize the high-quality development of cooperative economy. In this paper, we first use the entropy power method to measure and characterize the social network embeddedness, and then use the OLS regression model to analyze the influence mechanism of social relationship network embeddedness on the mechanism of wage income distribution and access to employment information of cooperative members, and explore the moderating role of environmental dynamics. The experimental results show that there is a certain strength gap in the external relationships of the social network of rural cooperative members, and the level of social relationship network embeddedness among samples from different regions is polarized. At the same time, the internal and external embeddedness of the social network of cooperative members has a positive effect on the efficiency of employment information acquisition, and there is a mediating role of the wage income distribution mechanism between the two. In addition, environmental dynamics moderates the two paths of action between social relationship network embeddedness and wage income distribution mechanism and employment information acquisition efficiency, but the moderating effect of environmental dynamics on capital income distribution mechanism and employment information acquisition efficiency is not significant. This study has certain guiding significance for the innovative development of cooperative economy.

Reasonable allocation of enterprise marketing resources can ensure that different target markets can be taken into account, but also to ensure that the newly developed markets can be cultivated, so as to maximize the economic benefits of limited resources. The article first combines the principles of marketing resource allocation, constructs a dynamic planning model of marketing resource allocation, and proposes a hybrid genetic algorithm improved by simulated annealing algorithm to solve the marketing resource allocation model. The effectiveness and superiority of the algorithm is tested through simulation and comparison experiments. And take an electrical appliance company as an example, based on the marketing resource allocation model to find the optimal program of the model, to explore the production and sales decision-making that is beneficial to the company. The results can be obtained, with the marketing resource allocation model set in the marketing department of the profit ratio from 10% to 25%, the total profit of the product is increasing, only the pursuit of product sales profit is maximized when the total profit can be obtained is about 17,956,500 yuan.

The essence of music is the carrier of human emotion expression, with the continuous deepening of music science and technology research, how to realize more accurate music emotion recognition has become the focus of public attention. This paper constructs a music emotion recognition model based on discrete emotion space (WLDNN_SAGAN). After pre-processing the collected audio data of vocal performances, the attention mechanism is introduced to weight and fuse the extracted low-level and middle-high-level music emotion features, and then the fused feature information is inputted into the WLDNN_SAGAN network to classify music emotions. The experimental results show that the model in this paper will improve the recognition accuracy of different emotions. Compared with the comparison model, the accuracy of this paper’s model reaches 60% and above on three DIFFERENT datasets. The emotional vein of Chinese folk song performance identified by the model is lightness towards sadness and sacredness, which is consistent with the historical facts of Chinese folk song creation. In conclusion, the emotional expression of vocal performance can be enhanced by understanding the cultural connotation, applying singing techniques and body language.

This study integrates fuzzy logic with DuPont ratio analysis reϐlecting ϐinancial ratios to construct enterprise proϐitability prediction model. The main indicators of DuPont analysis system are processed by principal component analysis (PCA) algorithm to obtain the calculation method of the mean value of enterprise comprehensive proϐitability indicators. The BP neural network is used to construct the enterprise proϐitability index model, and the momentum term is introduced into the model to improve the convergence speed of the BP neural network. The Takagi-Sugeno type fuzzy neural network is utilized to construct the enterprise development ability index model, and the enterprise proϐitability prediction model is constructed by combining the output structure of BP neural network. The relevant data of 792 listed enterprises in a certain industry in China’s A-share market are selected as the research objects of this paper, and the data are inputted into BP neural network and Takagi-Sugeno fuzzy neural network to obtain the output results of the model, and the output results are used as the input data of the ϐinal proϐitability prediction model to forecast the proϐitability of the enterprise in the next ϐive years. The experimental results show that the model in this paper can effectively realize the prediction of corporate proϐitability, which is signiϐicantly conducive to the sustainable development of enterprises and the adjustment and improvement of strategic policies.

The rapid development of artiϐicial intelligence algorithms makes them play an important auxiliary role in college English education. This paper deeply analyzes the application of artiϐicial intelligence algorithms in English education in colleges and universities, and constructs a method of analyzing students’ behavior in college and university English classrooms with computer vision as the algorithmic representative, which assists teachers to understand the state of students in the whole classroom.YOLOv7 network carries out multi-target detection in the classroom and improves the network for the deϐiciencies in the classroom environment. The improved K-means algorithm is then introduced to improve the DeepSORT multi-target tracking algorithm. Obtain the surveillance video data in the English classroom of Q college and build the dataset by itself, and design different experiments to verify the effectiveness of this paper’s algorithm respectively. Finally, the classroom behavior analysis method based on computer vision in this paper is applied to teaching practice to explore the practical application effect of the method. The results show that the improved method of this paper can signiϐicantly improve the performance of the target detection and tracking model, and the application of this paper’s method to the classroom time can accurately capture the classroom state of different students, and assist teachers in formulating different teaching strategies according to different classroom stages.

This paper focuses on the demand for intelligent assistance in English business writing scenarios and proposes an intelligent assistance system for English business writing based on image recognition algorithm and language model. The system is able to quickly extract image information related to the writing topic through the similarity vocabulary matching technology combined with the image retrieval recognition function based on CBLSTM-Attention model. The language model is utilized to make accurate vocabulary recommendation and expression for the writing scene and user input content, and finally construct the overall framework of the intelligent assistive system based on English business writing. The system performs well in terms of vocabulary matching accuracy and writing efficiency improvement, with an average matching accuracy of over 90%. Students’ quality of writing is essentially improved with the help of the system in this paper. The actual case study shows that studying under the intelligent assistance system, the post-test scores of English business composition of the students in the experimental class increased significantly by 9.9393 points (P < 0.05) compared with the average scores of the control class, and it is obvious that applying the model of this paper to the classroom teaching can lead to a significant improvement in the performance of the students, which demonstrates the good prospect of its application.

This study focuses on the innovation of teaching methods for Civic Education in colleges and universities, and provides a structured knowledge framework for teaching by constructing a Civic Knowledge Mapping and integrating course knowledge points. On this basis, a new classroom teaching mode is designed to integrate online and offline teaching resources to enhance student interaction and participation. A knowledge tracking model of key-value memory network (MKVMN) based on multifeature fusion is proposed to accurately track students’ mastery of Civics and Politics knowledge by capturing students’ multi-dimensional learning behavior characteristics. To optimize the recommended path for students’ personalized learning, an improved ant colony algorithm is introduced to generate personalized learning paths based on students’ individual differences. The experimental results show that when the number of learning units is 0-10 (pre-study period), the improved ACO algorithm model does not have obvious advantages for students’ learning, but when the number of learning units reaches 11-50, the difference between the experimental group students’ learning performance and the control group becomes more and more obvious, so it can be seen that the improved ACO algorithm can obviously improve the students’ Civic and Political Science learning performance. In addition, the IACS-PRA algorithm is especially effective in long path recommendation, which finds the optimal personalized recommendation path through a gradual approach to help students learn Civics and Political Science more efficiently, and provides a practical demonstration for the digital transformation of Civics and Political Science education in the new era.

The effective integration of intelligent interaction design and visual communication design education is an important attempt to improve the educational effect. This paper firstly constructs the evaluation system for the teaching effect of intelligent interaction design and visual communication design courses, and then establishes a set of evaluation models based on fuzzy logic inference algorithm. In the example application part, the G1-entropy weighting method designed in this paper is used to measure the weights of each influence index, followed by an empirical study using School A as an example, and finally the multiple linear regression analysis is used to make further exploration on the influencing factors of the teaching effect of the course. The study concludes that in the subjective weight calculation experiment, it is found that the weight of external influences accounts for the highest proportion of 0.277, that is, experts believe that the overall planning has a strong influence on the course effect. Further, the regression modeling yields that learning interest, curriculum, faculty, teaching content, and practical activities have significant positive correlation with teaching effectiveness.

Influenced by the backward management methods and other factors, the integration and sharing of digital educational resources in colleges and universities have certain problems, and cannot give full play to the advantages of digital educational resources in colleges and universities. Based on this, this study proposes a targeted digital education resources integration strategy, using particle swarm algorithm to optimize the sorting of digital education resources, to obtain high-quality digital education resources, introducing fuzzy clustering algorithm and combining with the principle of decision tree, to accurately classify and integrate digital education resources. On the basis of realizing the integration and classification of digital educational resources, a digital educational resource sharing model is formed to promote the effective use of digital educational resources. The digital educational resources integration strategy proposed in this paper is adopted to carry out the application practice of digital educational resources integration and sharing in S colleges and universities. The mean values of the three dimensions of students’ learning attitude, teachers’ teaching, and teaching effect in S colleges and universities reached 3.48, 3.97, and 3.74, respectively, and this paper’s digital educational resources integration strategy method has a positive positive impact on the dimensions of students’ learning attitudes, teachers’ teaching, and teaching effect in Civic and Political Education in S colleges and universities.

With the development of globalization, the cross-cultural market is facing needs such as diversification and personalization of consumer demand. Based on the theory of market segmentation, the study proposes an ant colony algorithm to improve the market segmentation model of K-means clustering, and examines its effectiveness. Further, a personalized recommendation algorithm based on multivariate dynamic user profiles is proposed to recommend products to target users more accurately. A reliable simulation environment is constructed based on the KuaiRec dataset and the classical LastFM dataset to properly evaluate the performance and effectiveness of the model on the recommendation platform. Through the K-means ant colony clustering algorithm proposed in this paper to divide the interest information and attribute information of users, the users as a whole are classified into specific categories, and the online_reward value of the personalized recommendation algorithm based on multivariate dynamic user profiles proposed in this paper fluctuates from 50.05 to 50.49, which is a significantly superior performance. As a result, this paper concludes that crosscultural marketing strategies should be marketed at four levels: product, price, channel, and promotion, in order to adapt to regional cultures, attract consumers, and build consumer loyalty and satisfaction.