Appropriate use of emotions as a means to intervene in students’ sports behaviors in physical education can promote individuals to form correct concepts of sports and physical exercise. In this paper, in order to construct an emotion intervention model, a cross-temporal adaptive graph convolution network (CST-AGCN) model for whole-body limb emotion recognition is proposed by using the method of spatio-temporal graph convolution. The model was applied to the first stage of negative emotion intervention, after which the appropriate intervention strategy was selected from the intervention strategy library. Then the system was used to assist the teacher in completing some of the intervention initiatives. Finally, based on the empirical study and the system, the learners’ classroom status after the intervention was analyzed again. In addition the study also designed strategies related to enhancement of students’ mental health to further promote students’ physical and mental health. After applying the emotional intervention model and mental health enhancement strategies to the second year (1) class of Secondary School S, this group of students showed significant differences in subjective experience, emotional vitality, body value, interpersonal perception, and dilemma coping, and their mental health was significantly improved. Physical education scores were 7.96 points higher compared to the traditional teaching class, and anxiety decreased significantly. It indicates that the intervention model and mental health enhancement strategies in this study can reduce students’ anxiety behavior and have a more significant relief of students’ negative emotional symptoms such as anxiety and depression, thus promoting the quality of physical education teaching.

This paper constructs a scientific and systematic model for evaluating the quality of Civic and Political teaching in physical education courses with the core concept of establishing morality and combining the intrinsic requirements of collaborative parenting between physical education courses and Civic and Political education. The evaluation indicators use the hierarchical analysis method to assign weights to the established indicators, and at the same time, the consistency test is carried out to ensure that the weights are assigned reliably. The evaluation model is applied to a sports college, scored by questionnaire survey, and combined with the fuzzy comprehensive evaluation method to realize the rating division of the teaching quality of the college. At the beginning of the study, the first-level indicator “Chinese sportsmanship” was rated by experts as low, with a mean value of 2.4, so it was revised to “professionalism”. The importance of the indicator “ideal belief” compared with other level 1 indicators in the evaluation model ranges from 2.24 to 2.65, with the highest weight of 0.308. A university implemented the evaluation model in this paper, and the quality of the university’s comprehensive sports ideology teaching was rated at 4.36 points, which is a good rating. Among them, most students rated the secondary index under “ideal belief” as excellent. The results of the study can be used as a theoretical basis and a practical tool to promote the design and evaluation of the Civics teaching in college sports courses.

The recent frequent occurrence of students’ psychological crisis events has drawn widespread attention to mental health education in colleges and universities. Based on students’ behavioral data, we use big data and data mining technology to model and analyze students’ daily behaviors, complete the construction of students’ social intimacy features based on Dijkstra’s algorithm, use the C4.5 decision tree improvement algorithm based on variable-precision rough set to realize the identification of students’ psychological problems, and analyze the intervention paths of students’ psychological problems and the evaluation of the results of the intervention. The proposed method can recognize students’ psychological problems more accurately, and the recognition accuracy of different levels of psychological problems reaches more than 72%, which is significantly higher than other classification methods. Learning anxiety, loneliness tendency and terror tendency of students in the intervention group were significantly reduced after the psychological intervention (P < 0.05), and the overall factor scores decreased by 9.85%, and the level of mental health was answered to be improved, which reflected the effectiveness of the proposed mental health intervention. The experiment proves that the model in this paper can effectively identify students with psychological abnormalities, and the proposed intervention path for students' psychological problems has a positive impact on the development of students' mental health.

Aiming at the problems of poor point cloud data fusion in traditional MLP models, this paper proposes a multimodal 3D target detection network based on KANs. A KANDyVFE encoder incorporating a fusion layer is designed with KANs as the backbone, and a self-attention mechanism is used to dynamically fuse point cloud features. Two datasets, KITTI and WaymoOpen, are selected as 3D target detection datasets to explore the performance level of the algorithm through controlled experiments. Based on ablation experiments, the effectiveness of the KANDyVFE encoder and the self-attention fusion module is verified. The proposed algorithm achieves 80.72% and 80.23% 3DmAP and 3DmAPH on the WaymoOpen dataset for LEVEL_1, which is 2.14% and 2.17% better than the closest BtcDet method, and achieves the same advanced performance on LEVEL_2. When the KANDyVFE encoder module is not used, the 3DmAP and 3DmAPH are only 72.36% and 74.35%, respectively, and the addition of the KANDyVFE encoder and the self-attention fusion module achieves 91.33% and 92.09% for 3DmAP and 3DmAPH, respectively. The experimental results validate the effectiveness of KANs in point cloud applications, and the ablation experiments further demonstrate the performance improvement brought by the designed modules.

Thanks to the wave of digital economic globalization, the business development of cross-border e-commerce platforms is in full swing. This paper aims to promote the development of e-commerce personalization and launch the research of consumer behavior characteristics. This paper utilizes the concept of entropy in information theory to modify the weights of user feature vectors, so as to make up for the inadequacy of the K-Means algorithm in expressing ambiguous clustering information. Combined with the data samples, the consumer behavior prediction model is established. For the dynamic clustering of customer groups, construct the customer segmentation model based on the improved K-Means algorithm. Combined with the time series prediction model, complete the formation of the spatio-temporal data mining model of consumer behavior. The model is used to mine the consumer behavior dataset of a cross-border e-commerce platform, and the clustering analysis yields four precise consumer group portraits. In this paper, by mining and analyzing the characteristics of consumer spatio-temporal data, the cross-border e-commerce platform is provided with more accurate user insights and marketing optimization solutions.

This paper takes ten economies as examples to analyze and assess the current situation of their international trade development through RCA, MS and TC indexes. On the basis of Porter’s “diamond model” theory, a comprehensive evaluation index system of international trade competitiveness is set up in combination with the actual situation. The entropy value method is used to measure the comprehensive index of international trade competitiveness, and the influence of various influencing factors on international trade competitiveness is empirically studied based on the principal component multiple regression analysis. The results show that the U.S. international trade competitiveness is far ahead, with an average score of 3.67 in 2020-2024, and the lowest score is Singapore, with a score of only -2.17. The degree of explanation of international trade competitiveness of the four factors reaches 98.9%, and all of them have a promotional effect on the international trade competitiveness, in the following order: factors of production>enterprise strategy and competition>related industries>demand factors.

The construction and opening of high-speed railroads have brought new development opportunities to China’s ethnic regions, which are economically backward but rich in tourism resources. From the perspective of the impact of high-speed rail on regional tourism, this paper briefly analyzes the homogenization effect and accessibility effect that the construction of high-speed rail brings to the corresponding region. Accordingly, it puts forward the relevant research hypotheses on the impact of high-speed rail on regional tourism and analyzes the current situation of tourism market development in China’s A ethnic region. Under this premise, the model of high-speed railroad influence on tourism development level is designed and relevant research variables are selected. Based on the model, the empirical analysis of the impact of high-speed railroad on tourism in ethnic region A is launched. The study points out that the opening of high-speed railroad significantly promotes the total tourism income of ethnic region A at the 1% level, i.e., the opening of high-speed railroad has a positive positive effect on the tourism development of ethnic regions.

Aiming at the complexity of mental health assessment for students in colleges and universities, this paper proposes an innovative framework that integrates social sentiment analysis and multi-branch neural networks. A multilevel mental health assessment system is constructed through cross-modal feature interaction CNN+BiGRU with heterogeneous graph structure modeling. In the model design, image feature extraction is pre-trained by five-branch CNN structure ViT, text features are fused by dynamic word embedding with multi-scale convolution, and a virtual node and metapath-driven heterogeneous graph neural network H-GNN is introduced to strengthen the global relationship modeling. Experiments show that the model achieves 89.7% and 91.2% accuracy on Twitter-15 and Twitter-17 datasets, respectively, and the F1 values are improved by 3.24% and 2.32% from the optimal baseline BICCM. In the actual college mental health monitoring, the model successfully captured the time-series fluctuations of depression index and anxiety level, and found that the rational-perceptual dimension was highly correlated with the examination cycle, with 0.69 during the midterm examination and 0.68 during the final examination. Through the ten-fold cross-validation comparison experiments, the model significantly outperforms the cutting-edge models, such as MIMNBERT, EF-NET and so on on the weighted average index, with an average accuracy rate of 99.02% and F1 value of 98.08%. The study shows that the framework provides a highly accurate and interpretable technical solution for mental health risk early warning, which is especially suitable for dynamic monitoring scenarios in universities.

Taking the perspective of new quality productivity, this study explores the promotion effect of the intermingling of intelligent computing and traditional culture on the cultivation of innovative talents, and constructs an evaluation system containing four primary indicators and 14 secondary indicators of educational activities, student practice, collaborative innovation and teaching resources. The cloud integration model is used to deal with the ambiguity and randomness of the complex system, and the network hierarchy analysis method ANP is used to determine the weights of the indicators and reveal the dynamic association of each element. It is found that: the indicator B2 of student practice category has the highest weight of 0.329, in which the number of awards of C5 innovation competition and the number of C4 students’ project participation are the core driving factors, with the weights of 0.103 and 0.078, respectively. the cloud integration model verifies the scientificity of the evaluation system. The evaluation value of the traditional culture innovation talent evaluation system constructed in this paper is 0.798, and the integrated cloud model belongs to “very good” grade. However, the mapping intervals of C14 Resource Library Call Frequency and C13 Teacher Integration Background are low, 0.346 and 0.413 respectively, which need to be adjusted and optimized. The innovative talent cultivation program of colleges and universities constructed in this study can make up for the shortcomings in traditional talent cultivation performance evaluation, has certain practicality and effectiveness, and helps to improve the quality of traditional culture innovative talent cultivation.

Under the background of economic transformation and high-quality development strategy, the coordinated development of regional economy and precise regulation of fiscal policy have become a hot spot of concern. This paper constructs the evaluation index system of regional economic high quality development and evaluates the level of economic high-quality development in the recent 10 years by using the entropy weight-TOPSIS method. Combined with Dagum Gini coefficient and spatial autocorrelation test, we study the spatial correlation of economic high-quality development among regions. The benchmark regression model and mediation effect model are constructed to calculate the effect of fiscal policy on the level of regional economic high-quality development, and to judge the effectiveness of fiscal policy regulation path. The study shows that the 30 provinces in China can be divided into different regions according to the level of high-quality economic development, and each region presents different types of characteristics such as “high – high”, with significant differences. The variables in the benchmark regression model and the mediation effect model are correlated at the 1% level, and pass the smoothness test when the difference is of the 0th order. Fiscal policy at the regional level has a positive contribution to the level of high-quality economic development, but at the same time is affected by the original level of development of each region.