Along with the development of the times, online classroom teaching activities have been carried out in different degrees and frequencies in various schools, and the gradual advancement of education informatization has improved the software and hardware environment of online classroom and other forms of teaching. The study designed a 21-item questionnaire related to English online classroom learning and selected all the students who participated in English online classroom teaching in a school for the survey. After collecting the questionnaire data, factor analysis and multiple stepwise regression model were used to conduct multivariate statistical analysis on the English online classroom data. And on this basis, the teaching plan was adjusted according to the actual learning behaviors of the high, medium and low risk level students themselves respectively to achieve personalized teaching. The results show that students’ satisfaction with the English online classroom is high, and that pre-course homework analysis, group learning, formative learning evaluation, students’ independent learning ability and online learning resources are the key positive factors affecting the learning effect of the English online classroom, with the influence coefficients of 0.036, 0.055, 0.048, 0.044, and 0.062, respectively. At the same time, after the optimization of teaching strategies, the students’ logged-in learning behavior, participation rate in interactive test questions and grades were significantly improved, proving the effectiveness of the strategy.

In today’s era, the transformative power of computing is highlighted, and computational thinking has become the core literacy and essential ability of learners, while computer education is an effective carrier for cultivating computational thinking. The article firstly researches the theory related to collaborative filtering and generative adversarial recommender system. Then it combines SeqGAN with traditional CF algorithms, proposes to use sequence generative adversarial network for missing data prediction, and makes appropriate improvements to SeqGAN to make it suitable for generating scoring data, and then further designs a computer teaching system based on this model. The article launches performance testing experiments on Ali’s real dataset UserBehavior, and conducts experiments on the effect of computer education with the students of computer application major in a secondary school as the research object. The results of the study show that in the comparative analysis of the pre-test and post-test of computational thinking of the experimental class, the mean of the total score of computational thinking of the experimental class in the pre-test and post-test is 71.17 and 78.35, respectively, and the post-test is more than 7 points higher than the pre-test. It can be concluded that the teaching model of multilevel computational modeling designed in this paper promotes the development of students’ computational thinking and academic performance, improves students’ learning attitudes, and increases classroom participation.

With economic globalization and the increasing complexity of inter-enterprise business linkages, corporate financial systems have gradually taken on the characteristics of complex networks. This paper firstly gives an overview of the complex network and introduces its basic topological properties, such as clustering coefficient and path length. After that, through the principal component analysis method, the enterprise financial risk early warning indicators are identified, and the key indicators are screened to improve the early warning accuracy. Based on these properties, the financial risk conduction network model of complex enterprises is constructed, the characteristics of the network are analyzed, including network density, centrality distribution, etc., and the effect of financial efficiency enhancement of complex enterprises under the optimization of topology computation is verified in real cases. The results show that most of the financial risk indicators of enterprises have strong correlation, and the degree of centrality of 9 indicators such as “gearing ratio and quick ratio” is more than 50%. In addition, the indicators of “current asset turnover ratio, interest coverage multiple, net profit growth rate” can play the role of intermediary and bridge, and the risk transmission effect among the indicators is high. The threshold value of 0.65 is the watershed of the changes in the financial structure of enterprises, and most of the financial risks in the network have a high degree of similarity in the financial structure when the degree value is 70, and it is negatively correlated with the coefficient of agglomeration, and the coefficient of agglomeration decreases with the increase in the intensity of the points.

In order to explore the deficiencies in the teaching process of marketing majors in higher vocational colleges and further improve the teaching quality of marketing majors in higher vocational colleges. This paper utilizes the improved ID3 algorithm to construct the SLIQ data mining algorithm to improve the teaching quality of teachers of marketing majors in higher vocational colleges and universities. Using ID3 algorithm to build a decision tree to get the portraits of teachers and students, at the same time, in order to reduce the computational complexity of ID3 algorithm and the problem of multi-value bias, the concept of sample structure vector similarity is introduced, and the degree of information gain is optimized to get a more reasonable decision tree. On this basis, based on the improved ID3 data mining algorithm, a teaching quality assessment system for senior marketing majors based on SLIQ algorithm is designed, which identifies important factors affecting teachers’ teaching quality by mining a large amount of data in the teaching process.The AUC value of the SLIQ data mining algorithm is 0.98, which can effectively improve the algorithm’s generalization ability, and it has an excellent performance in the teaching quality assessment task. The performance is excellent. In this paper, we systematically identify “the principles of marketing” and “the degree of seriousness of teachers’ homework correction” as the key factors to improve the teaching quality of marketing teachers. It provides a scientific basis for improving the quality of teachers’ teaching.

Visual communication design requires that feeling information and exchange of information must be conveyed efficiently and accurately. In this paper, we design a robust principal component sub-analysis visual enhancement algorithm based on improved Retinex. The algorithm transforms the image to the logarithmic domain so that it satisfies the decomposition condition of RPCA. After the RPCA decomposition model to get the low-rank component and sparse component, and will use adaptive gamma correction algorithm for the low-rank component for contrast enhancement, the two components are combined and then inverse transformed in the logarithmic domain to get the enhancement results. To avoid color distortion, the input image is converted to HSV color space to separate illumination information from noise. The model uses the inexact augmented Lagrange multiplier method (IALM) to solve the optimization problem, which leads to a significant improvement in the decomposition speed. The performance of the designed algorithm is verified on the dataset, and it is found that after the color equalization process for overexposed images, the gray value distribution is more uniform, and the image shows a better sense of brightness and visual effect after the contrast is increased. The algorithm scores 0.4648 and 0.7577 in UCIQE and UIQM respectively, which are ranked first among all algorithms and have better visual effect and information communication efficiency.

In recent years, China’s research investment in colleges and universities has gradually increased, but not much research and exploration has been done on the construction of the evaluation index system for the integration of industry and education. The state, society, industry and so on have brought rare opportunities for the implementation of in-depth integration of industry and education, which also indicates the imperative of the development of integration of industry and education. Based on the practical significance of educational evaluation, this paper applies the CIPP model to the construction of the quality evaluation system for collaborative education and training in university modern industrial colleges in view of the high degree of fit between the CIPP model and the process of university-industry-industry fusion activities in university modern industrial colleges. The recursive hierarchical structure is established according to the established index system, and the weights of the index system are calculated through the consistency test. The factor loading matrix of the first three principal components is constructed, and the modern industrial colleges are evaluated according to the principal components, and the mean values of the principal components 1, 2, and 3 are 0.27, 0.096, and -0.0186, respectively.In the calculated quality evaluation results of the integration of industry and education in modern industrial colleges, the score of educational and teaching achievements of the modern industrial colleges in Zhejiang Province is relatively low at 85.8439, which indicates that there is a gap in educational and teaching achievements, and there is a need to further improve the education and teaching achievements of modern industrial colleges. In addition, there are differences in the evaluation of the quality of industry-education integration in different modern industrial colleges in Zhejiang Province.The results of this study indicate that it is necessary to further optimize the construction path to meet the actual needs of industry-teaching integration in Zhejiang Province.

Lung cancer is the most common malignant tumor in humans and the leading cause of cancer-related deaths worldwide. In this study, we focused on the immune cells in the microenvironment of lung cancer at the protein expression level by IHC as well as mIHC techniques to explore the spatial distribution characteristics of immune cells within the tumor. To predict the prognosis of NSCLC patients and their potential response to immunotherapy, a machine learning-based immune-related prognostic model for lung cancer was constructed by combining Cox regression analysis, random survival forest and XGBoost algorithm, and the effect of the prognostic model was verified on the relevant dataset. The results showed that there were some differences in the immune cells between lung adenocarcinoma and lung squamous carcinoma in the lung cancer microenvironment, and the spatial distribution heterogeneity of CD3+ T cells and MHC class II antigen-presenting cells was higher in lung adenocarcinoma (P<0.05).The overall survival of high-risk patients was lower than that of the low-risk group in both LUAD and LUSC (P<0.01), and the immuno-associated prognostic model of lung cancer had a stable performance in the AUC value in multiple independent cohorts with stable performance, and the IRS model maintained high accuracy and stable performance in the training set and test set, which indicates that IRS has great potential for clinical application.

Rhythm matching of music and dance is an important research area in cross-modal analysis. In this paper, a music and dance rhythm matching algorithm based on time series analysis is proposed to extract the time series features of music and dance, and a genetic algorithm is used to determine the correspondence between music and dance movements to reflect the degree of correlation between changes in music and dance rhythm movements. In order to improve the matching and smoothing degree between the dance movement time series and the music time series, a constraint-based dynamic programming algorithm is introduced. The experimental results show that the model performs well in the matching degree and matching efficiency enhancement between dance movement time series and music time series, and its matching efficiency is 2-3 times of the traditional method. It shows high practicality in dance choreography and music matching, and can match any music clip with smooth and beautiful dance movements. The research in this paper provides new technical means for dance choreography and music matching, which will further optimize the transition harmony between music time series and dance movement time series.

This study takes the physical properties of high temperature devices as a starting point and the experimental apparatus used to obtain the study samples. The heat transfer process can be categorized into heat conduction, heat convection and heat radiation depending on the mode of contact. Under the theoretical support of the first law of thermodynamics, the nonlinear partial differential equations of the heat transfer characteristics of the high temperature devices are determined, and the above equations are analyzed by numerical simulation with the help of ANSYS software. When the thickness of the device is 1um, 8um and 15um, the heat transfer temperature and the power of the heat source show a monotonically increasing trend, in addition, when the thickness of the device is a fixed value, the spacing of the heat source and the heat transfer temperature show a nonlinear monotonically decreasing, and the present study has an important practical significance for improving the heat transfer performance of high temperature devices.

In order to explore the relationship between multi-source terrain features and lightning activity in Inner Mongolia, monitoring data and digital terrain elevation data of thunderstorm activity in Inner Mongolia from 2014 to 2025 were collected, and the spatio-temporal data mining method of mathematical and statistical analysis was used to analyze the distribution characteristics of lightning activity in Inner Mongolia. Based on the selected terrain feature factors, the machine learning method of multiple regression analysis is used to establish a research model of multi-source terrain features and lightning activity for quantitative analysis. The results show that the frequency of ground flashes in Inner Mongolia is mainly concentrated in May-October, accounting for more than 92% of the whole year, and the seasonal characteristics of its ground flash activities are significant, and the current intensity is mainly concentrated in the range of 20-40 kA. Correlation analysis reveals that multiple features of multi-sourced terrain are positively and negatively correlated with the frequency of lightning ground flashes and the current intensity (p < 0.05), and the prediction error of the constructed regression model for the ground flashes' frequency and the current intensity is 7.31%. The prediction errors of the constructed regression model on ground flash frequency and current intensity are 7.31% and 5.08%, which can provide a reference for lightning disaster prevention and mitigation in Inner Mongolia.