bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Regulation of Intelligent Elderly Service Laws in the Age of Artificial Intelligence
Authors: Ning Wanyu , Wei Xize
Published: February 2024

The artificial intelligence senior care model brings about work efficiency improvement and human resource cost saving to old care service enterprises and government departments. Taking Maslow’s hierarchy of needs theory as an entry point, the article systematically combs through the needs, legal source relations, and potential risks of intelligent senior care services. To accurately assess the relevant influencing factors of the legal regulation of innovative senior care services, linear regression and seemingly uncorrelated regression models are introduced. The model parameters are estimated by Bayesian estimation method, and the empirical analysis is conducted based on the baseline regression and seemingly uncorrelated regression models. When the local government pension policy decreases by every 1 percentage point, the degree of legal regulation of innovative pension services will decrease by 0.598 percentage points. In addition, in the likelihood uncorrelated regression model, the influence coefficient of education level is −0.372, which is 38.81% lower than the benchmark regression model. To better guide the standardized development of innovative senior care services, the central and local governments must actively introduce relevant policies to help the public better understand and accept the advantages of intelligent senior care services.
Modeling and tracking control of underwater unmanned underwater vehicles for deep-sea marine environment detection
Authors: Wang Qiusheng , Wang Jingnan , Qi Xiangdong
Published: February 2024

This paper focuses on enhancing target tracking techniques for unmanned underwater vehicles (UUVs) used for deep-sea exploration, especially for the latest advances in digital technology. The article begins with an innovative UUV target tracking algorithm improvement based on the correlation filtering algorithm. Introducing the spatio-temporal regularization method and penalty function optimizes the filter parameters and enhances the algorithm’s performance. Then, a new objective function is constructed by combining the optimal feature weights and baseline function calculated by the dynamic feature weighting method, which effectively excludes the influence of interference peaks in the algorithm. In addition, by applying the Gaussian correlation method, the article further improves the algorithm’s robustness. When the obstacle avoidance ability of the algorithm is analyzed, it is found that the control error of the obstacle avoidance speed in both X and Y directions is less than 0.1 m/s, and the difference between the actual angle and the desired angle of the attitude angle is less than 0.5 degrees. This indicates that the algorithm can effectively avoid all obstacles and accurately track the target. The target tracking experiments show that the tracking performance scores of the algorithm are generally higher than 9, and the tracking accuracy is more than 95%, which is significantly better than other existing algorithms. The optimized tracking algorithm proposed in this paper not only provides essential technical support for the development of unmanned underwater vehicle technology, but also provides valuable reference for the future application and practice of the algorithm.
Analysis of new energy vehicle battery temperature prediction by combining BP neural network
Authors: Xie Yuyang
Published: February 2024

With the rapid development of the new energy industry, the safety research of battery technology has become a key topic. This paper focuses on the temperature prediction of new energy vehicle batteries, aiming to improve the safety and efficiency of batteries. Based on the new energy vehicle battery management system, the article constructs a new battery temperature prediction model, SOA-BP neural network, using BP neural network optimized by SOA algorithm. This model can accurately predict the battery temperature, and the effectiveness of its temperature control is verified through experiments. The results show that the SOA-BP neural network model outperforms the traditional BP, CNN, and RNN models in temperature prediction. Regarding evaluation indexes, the model’s root mean square error (RMSE), mean absolute error (MAE), and R2_Score are 0.953, 0.909, and 0.837, respectively. It is worth noting that the model can effectively regulate and control the battery temperatures at different temperatures, ensuring that the maximum temperature difference of each battery is maintained within 5°C. The model can also be used to predict the temperature of the batteries in different temperatures. This battery temperature prediction model not only provides an effective means for predicting and controlling the battery temperature of new energy vehicles, but also provides an essential reference for improving the vehicle’s performance and developing energy management strategies. This study offers a new solution for the safety and efficiency of new energy vehicle batteries.
Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions
Authors: Zhao Yu , Wen Lizhi , Wang Lei , Wei Mingjiang , Wang Yu
Published: February 2024

This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture system including network layer, coordination layer, control layer and physical layer, and based on environment sensing technology and 5G network, a layered control structure for autonomous driving vehicles is established. In the study, the inter-vehicle distance control strategy is proposed, and combined with the theory of metacellular automata, the study constructs an intelligent networked fleet model, which adopts the full speed difference model and adaptive cruise control vehicle following model. The simulation analysis is carried out through the actual data of a section of G6 Beijing-Tibet Expressway, and the simulation environment is a two-lane highway of 3000m, considering different CAV vehicle penetration rates (0% to 100%). The results show that with the increase of CAV penetration, the traffic flow and the congestion significantly improved. The intelligent Connected Vehicles fleet performs well in the obstacle avoidance task, effectively reducing unnecessary lane changes and shortening the congestion time compared to the conventional system, with the distance error controlled within 1m. This study shows that the intelligent connected vehicles-connected fleet has significant advantages in improving traffic flow’s operational efficiency and safety.
Brain Liberation - An Anthropological Reflection on the Threat of Artificial Intelligence
Authors: Zhou Yun , Luo Qing
Published: February 2024

This study examines the threat of AI to the emancipation of the human brain, with a particular focus on its impact on human logical thinking and creativity. It reveals the centrality of AI in knowledge engineering by analyzing expert systems constructed by the Symbolist school, which emphasize the role of knowledge bases and reasoning machines by simulating human logical thinking. The study also examines how AI affects modern human subjectivity, utilizing the “Valley of Terror” curve theory to explain the changes in human attitudes toward AI Further, the study introduces human brain-based AI models, such as the application of back-propagation algorithms in neural networks, as well as a cognitive-based model of knowledge operations in the human brain. The results show that the development of AI poses a threat to human brain power, especially in information processing and decision making. AI can replace human brain functions, leading to human logic and creativity degradation. For example, the application of ChatGPT in education can significantly improve students’ test rankings and reduce the reliance on individual thinking skills. In summary, AI may lead to the degradation of human logical thinking and creative ability while improving efficiency and convenience, affecting the overall development of human beings. Therefore, the focus should be on balancing the relationship between the utilization of AI and the development of human brain capacity, significantly to enhance the cultivation of human creativity and imagination in education and innovation.
A study of the impact of financial news media coverage patterns on stock price fluctuations
Authors: Xue Ziyu , Yin Lijie , Wang Junkai
Published: February 2024

This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.
Spatial Design Research on Digital Media Technology Intervention in Immersive Art Theory Exhibition Venues
Authors: Zhao Xi
Published: February 2024

The article takes the application of digital media technology in the spatial layout design of art exhibition halls as the entry point, and utilizes Web technology and the integrated simulation platform of urban VRGIS to establish the virtual space of immersive art exhibition halls. The article obtains binocular imaging data through binocular visual imaging model and solves the three-dimensional coordinate system by using the method of polar geometric constraints, and then reconstructs the three-dimensional space through the point cloud data to realize the design of the virtual space of the immersive art exhibition hall. The article evaluates the effectiveness of the immersive art gallery virtual space from three aspects: interactivity, DPI recovery value and eye movement data visualization. It is found that users are most effective with direct interaction within an interaction depth of 1.1 m to 1.7 m. In comparison, ray interaction is more appropriate when the interaction depth exceeds 2.3 m. Applying the method of this paper, the global DPI recovery value of the immersive art gallery can reach 89.67%, and the pupil diameter of the subjects can reach about 5.79 mm in the virtual space test over 160 seconds. In this paper, digital media technology can more effectively realize the immersive presentation of art theories, to better satisfy people’s growing spiritual needs and provide more diverse cultural experiences.
Mining and Application of Digital Health Elements in Higher Education Student Management and Education
Authors: Yu Haibo
Published: February 2024

This study explores the mining and application of digital health elements in higher education student management and education, refining student user profiles through data mining techniques and applying them to student management and education to improve the accuracy and effectiveness of education management. The basic process of data mining, including data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge representation, was first carried out in the study. Then, a clustering recommendation algorithm based on user characteristics was designed to construct a clustering model of user interest preferences by calculating the distance between user attributes and filling the rating matrix using rating time and item type. Then, the study constructed a student user profiling system and analyzed techniques such as fuzzy C-mean clustering, association rule algorithm and user-based collaborative filtering. The study results show that applying digital health elements in student management and education helps identify potential association patterns of students’ mental health problems. For example, in the W vocational school case study, the association rule algorithm found that the sense of learning stress and compulsion were the most frequent combinations of psychological problems among students, with support levels of 0.7451 and 0.6518, respectively. In addition, the study evaluated the effects of educational management interventions on the mental health of higher vocational students, and found that, after adopting student user profiles for educational management interventions, the experimental class students’ mental health scores for each variable were 0.602 to 1.113 points higher than those of the control class, which significantly improved the students’ psychological quality.
Assessment of the Effectiveness of Intelligent Computing Modeling Application Environment for Sustainable Integration with Civic Education in Colleges and Universities
Authors: Chen Xiaoyan , Zhou Yamin
Published: February 2024

Incorporating the concept of environmentally sustainable development into the Civics program in colleges and universities is an important initiative to promote the service of environmental sustainability by the Freshmen. Innovative teaching mode of ecological sustainability and civic education, and the deep integration of green development concept and civic education are crucial to cultivating high-quality talents with environmental sustainability. In this paper, the collected data were standardized and defuzzified, and the CRITIC combination assignment method was used to assign weights to the indicators to obtain the optimal combination weights. Based on this, the indicator data’s gray correlation coefficient and correlation degree were calculated to construct the effect assessment model. The evaluation content for integrating the cultivation of the concept of environmental sustainability and ideological education is proposed in the paper, and the effect evaluation of this was implemented for 11 years. The evaluation results show that in the three classes of college A in the pilot area, the integration of the concept of environmental sustainability and Civic and Political education of college students was significantly improved, and the mean values of the measured improvement were 4.96, 5.05, and 4.95, respectively, which showed a better effect. Among the dimension scores, the mean value of the scores of the three classes in the five dimensions is 3.624.This result indicates that the cultivation of the concept of environmental sustainability has a significant enhancement effect on the performance of Civic and Political Education, and the integration of the two is having a significant impact. The study in this paper incorporates the concept of environmental sustainability into the ideology and political science curriculum of colleges and universities is an effective strategy that not only enhances students’ environmental awareness, but also strengthens the effectiveness of ideology and political science education.
Coordinated Cultivation of Innovative and Entrepreneurial Knowledge and Ability of College Students Based on Markov Modeling
Authors: Yang Dan , Zheng Sisi , Cheng Si , Zhou Qi
Published: February 2024

Evaluating innovation and entrepreneurship ability measurement of college students is one of the critical areas of research on innovation ability cultivation in colleges and universities. In this study, based on the existing Bayesian knowledge tracking model, the parameter matrix of innovation and entrepreneurship knowledge point relationship is added to improve the standard knowledge tracking model. The CS-BKT model is proposed to provide college teachers with detailed mastery of students’ innovation and entrepreneurship knowledge. Then the corresponding state space is delineated according to the mastery situation, and the evaluation model of innovation and entrepreneurship ability measurement for college students based on polymorphic rewarded Markov chain is constructed. Combined with different transfer probabilities, the model operates on student data to measure students’ innovation and entrepreneurship ability. The results of the study showed that there was a significant difference between the acceptance and non-acceptance of innovation and entrepreneurship education in students’ innovation and entrepreneurship competence, and the level of innovation and entrepreneurship competence of students who had accepted it was 3.71, which was higher than that of students who had not taken it (3.53). There is a significant positive correlation between the social level of the place of origin, the individual level of entrepreneurial experience, and the rank of professional achievement and innovation and entrepreneurship ability, with p-values of 0.008, 0.003, and 0.001 respectively are less than 0.01. This study concludes the factors influencing the innovation and entrepreneurship ability of college students, which provides a reference value to the formation and development of the innovation and entrepreneurship ability of the students.
Research on the Quality Assessment Model of “Dual-Teacher” Teachers in Higher Vocational Colleges and Universities Based on Big Data Technology
Authors: Lou Yunjia , Xiao Huayi
Published: February 2024

To further analyze the content of “dual-teacher” teacher team construction in higher vocational colleges and universities, this paper focuses on teacher moral construction, teacher training, assessment and evaluation, and incentives for in-depth investigation. It is clear that higher vocational colleges and universities should distinguish between “dual-teacher” teacher quality assessment and ordinary teacher assessment, and put forward the gradient “dual-teacher” teacher quality team construction. Improve the support vector machine, use the binary tree to propose an evaluation model based on incomplete DBT-SVM, and combine multiple binary classifiers to solve the multi-classification problem of “dual-teacher” teacher quality evaluation. The optimal parameter combinations, i.e., C =28 and γ = 0.0534, are obtained using the kernel function and parameter tuning experiments, and the accuracy of the model prediction results reaches 94.325%, taking the “dual-teacher” teachers in a university in Y province as the specific evaluation object. This shows that the accuracy of this DBT-SVM-based evaluation method of “dual-teacher” teacher quality is good.
The Beauty of Context and Its Expression in Chinese Language Literature: Based on the Perspective of Semantic Contrastive Analysis
Authors: Chen Xiaojing
Published: February 2024

This paper conducts an in-depth study centering on the beauty of context and its expression in Chinese language literature, mainly from the perspective of semantic contrastive Analysis. Firstly, the study utilizes various natural language processing techniques including LSTM neural network and GRU model to acquire and process textual semantic information by constructing a specific Chinese language contextual model. Then, an empirical analysis of Chinese language literature was conducted, and accuracy and recall were used as evaluation criteria to compare different Chinese language contextual models. The results show that the LSTM-GRU-based Chinese language contextual model significantly improves accuracy and recall. In addition, the model’s effectiveness in improving the ability of contextual Analysis in Chinese language literature is demonstrated through the review and Analysis of Chinese language literary works. Finally, the article proposes strategies to improve the ability of contextual Analysis in Chinese language literature.
Research on Accurate Teaching Reform of Japanese Language Education in Colleges and Universities with the Help of Big Data Technology
Authors: Li Yin
Published: February 2024

The development of Japanese language education provides a solid foundation for the friendly relations between China and Japan. In this paper, we first constructed a framework for optimizing teaching in Japanese language education based on big data and intensive learning intervention decision-making. We designed an achievement prediction model based on deep learning for Japanese language teaching effectiveness. The achievement prediction model is applied to the teaching achievement prediction and compared with the actual achievement, and its relative error range is between 0~0.2, compared with the traditional prediction model error reduction of about 0.2 or so in the Japanese language teaching achievement prediction analysis, the confidence and validity of the learning law and class performance are more significant than 0.8, the association rule is superior, and the optimized student prediction of the Japanese language teaching course learning achievement is basically distributed between 80 and 100 points, and the achievement level was significantly improved. This study provides an effective method for the precise reform of Japanese language teaching and realizes the prediction of the effectiveness of educational reform.
Role Shaping and Character Recognition of Cultural Education in Higher Education in the Context of Digitization
Authors: Luo Bin
Published: February 2024

Effective role shaping of cultural education in colleges and universities is of tremendous and far-reaching strategic significance to the development of socialist modernization. This paper establishes 3DBP-TBR method based on the 3D CLBP method with higher granularity to identify and analyze the role behavioral characteristics of teachers in higher education. It uses the elbow method to determine the optimal number of clusters in the clustering analysis, extracting and identifying the characteristics of teachers’ role behavior. By analyzing the role shaping of teachers, it was found that Teachers have the highest teaching support role index of 0.9548 points, and there is a significant difference in the dimension of teaching support characteristics of teachers teaching different courses (P=0.047<0.05). The analysis results in this paper lay a foundation for the study of role modeling and characteristics of college teachers, and the proposed role modeling suggestions also provide a reference for the future improvement of cultural education in colleges and universities.
Research on educational applications based on diagnostic learning analytics in the context of big data analytics
Authors: Zhang Naimin , Zhang Linlin
Published: February 2024

In the context of the significant data era, this paper explores the educational applications based on diagnostic learning analytics technology to improve personalized learning and teaching effects in the educational process. The study adopts a multidimensional feature fusion approach to construct a cognitive diagnostic model to predict learners’ knowledge status and future learning performance. Through actual data testing, the model can effectively predict the students’ knowledge mastery state and analyze the students’ learning process in depth. The experimental results show that the diagnostic model exhibits high efficiency and accuracy in predicting students’ knowledge mastery status, with an accuracy rate of 92.97%, significantly better than traditional teaching methods. In addition, the study explores the encoding method of learners’ multidimensional features and constructs a dynamic diagnostic model of test factors and student factors based on graph attention network. The study provides a new learning analysis and diagnostic method in the education field, which helps improve the effect of personalized learning.
Research on the Teaching Reform of Art and Design Courses in Colleges and Universities Driven by Artificial Intelligence
Authors: Zhao Yanyun , Xue Yujia
Published: February 2024

This paper focuses on analyzing the critical technologies of artificial intelligence technology in the creation and display of art and design teaching in colleges and universities, respectively, using image processing technology, image geometric transformation, and image special effects processing to realize the basic image design of art design, and using head-eye synergistic eye-movement multi-channel interaction and gesture interaction to form an immersive interactive art and design display device. Combined with the radar chart of the acceptance of AI technology in the course teaching, the influencing factors of the three dimensions of cognition, interest and Practice are explored. The OBE concept is introduced to propose a teaching reform program for art and design courses under AI technology. Practical application of the teaching program can improve students’ visual literacy in art and design, and its numerical difference is 0.3254 at the most. Thus, the teaching reform program of art and design courses in colleges and universities based on the OBE concept proposed in this paper is feasible to a certain extent.
The Infiltration and Integration of the Concept of Ideological and Political Education in the Employment of College Students in the Era of Big Data
Authors: Wang Yu , Zhou Yu
Published: February 2024

This paper studies integrating and penetrating the concept of Civic and Political Education in the employment guidance of college students to improve the effect of employment education in the context of big data. This paper uses genetic algorithm and projection tracking method to construct a measurement and evaluation model of Civic and Political Education and employment integration level. It is found that the coupling degree between Civic and Political Education and student employment in college A has increased year by year from 2010 to 2023, showing the effective integration of Civic and Political Education and jobs. The coupling degree risen from 0.136 in 2010 to 0.987 in 2023, indicating that the penetration and integration of Civic and Political Education in employment increased significantly. Strengthening the integration of Civic and Political Education and Employment Guidance can help improve the quality of student employment and student employment satisfaction. This study provides empirical support for integrating civic and political education and employment guidance in colleges. It confirms the importance and effectiveness of civic and political education in employment guidance in colleges and universities in the era of big data.
Analysis of the innovation path of news dissemination based on the multivariate data chain network
Authors: Wu Bin
Published: February 2024

This paper explores the innovative path of news dissemination based on multivariate data chain network, through the design and implementation of a multivariate data chain integrated planning system, to improve the transmission efficiency and accessibility of the data chain network, and then optimize the effect of news dissemination. The study designs a multivariate data chain integrated planning system, considering the constraints of delay and transmission, and uses Poisson distribution and queuing theory to analyze the access delay of the data chain. It is found that the access delay can be effectively reduced by controlling the generation and transmission process of data packets. Secondly, the C-RTT algorithm is proposed to optimize the synchronous time slot allocation in Link16 network, which is experimentally demonstrated to improve the time slot utilization by 1.64% to 6.57% and significantly increase the network capacity. Further, the multivariate datalink dissemination model outperforms the traditional news dissemination method regarding news content perception, environmental awareness, news reliability, and environmental behavioral intention. The application of multivariate data chain network has significant advantages in news dissemination and has substantial impacts on the development of news industry.
Exploring the Digital Innovation Development Mode of Chinese Sports Intangible Cultural Heritage
Authors: Zhang Zuqiang , Li Zhiqiang
Published: February 2024

This paper explores the innovative development of digitization of Chinese sports intangible cultural heritage, focusing on applying advanced technologies such as three-dimensional reconstruction and movement recognition to protect and pass on this unique cultural heritage. The article digitally captures and analyzes traditional sports movements through 3D reconstruction and movement recognition techniques. Subsequently, a feature extraction method is applied to capture the critical features of the movements and Bessel curves are utilized to enhance the realism of the virtual character model. The experimental results show that this method can effectively recognize and reproduce traditional sports movements, with a recognition success rate as high as 97.05%, and accurately determine the standard degree of the movements. In addition, this technique helps to reduce training costs and improve training efficiency. Through these techniques, not only can the intangible cultural heritage of sports be preserved and inherited, but also promote its integration with modern science and technology and open up new ways for its development.
Research on the Path of High-Quality Development of Sports Industry Driven by Digital Economy Based on Big Data Analysis
Authors: Bai Hongzhou
Published: February 2024

This paper focuses on how the digital economy drives the high-quality development of the sports industry, explores the new path of development using extensive data analysis, and provides theoretical basis and practical guidance for the innovative development of the sports industry in the digital era. The DEA-Malmquist model and comprehensive data analysis methods are used to measure the development efficiency of the sports industry. The results of the study show that the digital economy has a significant impact on the efficiency improvement of the sports industry, in which the effect on the efficiency improvement of the sports industry is the largest, and the regression coefficient reaches 1.153. In addition, the study also finds that the digital economy can significantly reduce the cost of the sports tourism enterprises, improve the cost profitability, and effectively promote the expansion of consumption. The study’s conclusion shows that the development of digital economy provides new development power for the sports industry, and can effectively promote the optimization of the structure and high-quality development of the sports industry. In this regard, it is recommended to strengthen the application of digital technology in the sports industry and enhance the digitalization level of the sports industry.
Research on Intelligent Management System of Gas Pipeline with Multi-source Data Fusion
Authors: Cao Xin , Tan Jianxin , Li Hao , Li Rui , Wang Yifan , Zhang Junfeng
Published: February 2024

Aiming at the current challenges of enormous scale, complex structure, difficult control and frequent accidents of city gas high-pressure pipeline network, there are still three aspects of difficulties in the risk monitoring and control of China’s city gas high-pressure pipeline network, namely, rough data, shallow assessment, and lack of power. This paper proposes an intelligent management system for gas pipelines based on C/S model and J2EE enterprise-level framework, in which the failure warning models of gas leakage, Gaussian plume diffusion, and fire and explosion are established. And the Kalman filter algorithm improved by DS evidence theory is used for intelligent fusion of Multi-source data, analyzing and screening the unified adequate information on data types, extracting state characteristics, classifying warning levels, and developing an integrated and visualized pipeline remote diagnosis and warning platform. In the simulation of the intelligent management system of gas pipeline, when the wind speed is 1.5m/s in winter, the ground surface is a safe area within 12.15m of the gas pipeline. When the maximum wind speed is 10m/s, the upper limit distance of the gas leading to fire and explosion is only 2.43m, and the hazardous range of the gas pipeline jet fire is within 12.69m. Relying on the gas high-pressure pipeline network in L city for practical experiments and applications, it provides technical support and decision-making basis for the construction of intelligent pipeline network, comprehensively improves the risk control capability of city gas high-pressure pipeline network, and has reference significance for the risk control of national city gas high-pressure pipeline network.
Study on the Strategy of Red Literature and Tourism Brand Communication Helping Study Tourism Economic Development in Liaoning Province under the Background of Internet
Authors: Cao Xue
Published: February 2024

Liaoning Province has a deep red cultural background and rich red tourism resources, and has an enormous potential for the development of red study tour. This paper establishes the evaluation system of the competitiveness of study tour development in Liaoning Province based on the explanatory structural model, solves the index weights through the coefficient of variation method, and gives the calculation of the competitiveness of study tour economic development. The regional differences in the competitiveness of research and study tourism economic development were measured by using the Thiel index, the spatial autocorrelation Moran index verified the regional correlation, and the TF-IDF algorithm was introduced to improve the LDA theme model thereby analyzing the emotional expression of the research and study tourism network text. Taking the tourism data of Liaoning Province from 2015 to 2019 as the research object, the data were quantitatively analyzed regarding development competitiveness evaluation, regional differences, spatial autocorrelation and emotional expression. The results of the study found that the weight of the red culture tourism market size in the economic development competitiveness of study tourism reached 29.64%, the peak and valley values of the regional differences in competitiveness from 2015 to 2019 differed by 0.0587, and significant differences were presented at the 1% level between cities. The frequency of red characteristic words reached 2,536 times. This indicates that the effective integration of red culture and study tourism in Liaoning Province effectively reduces the development differences between cities, and enhances the satisfaction of study students with red culture study tourism bases.
Linear regression analysis of the correlation between students’ physical education performance and academic achievement in the context of smart physical education in colleges and universities
Authors: Chen Jin
Published: February 2024

In this paper, the principles, algorithms and different linear regression models are sorted out and finalized to determine the multiple linear regression model suitable for this study. The different types of regression are synthesized through the elastic net algorithm. Based on the multiple linear regression model, students’ performance in language, mathematics and English was used as the dependent variable Y on behalf of academic performance, and five sports such as physical education and hygiene were used as the independent variable X, to carry out an empirical study on 3,800 students. From this, it was concluded that the contribution rates affecting language achievement were sports attitude, environment, and hygiene in the order of coefficients of 0.548, 0.312, and −0.266. In contrast, exercise habit [B=−0.032, t=−1.574, P=0.116] and sports knowledge [B=0.027, t=1.561, P=0.119] did not pass the test of significance. The contribution of influencing math achievement was in the order of sports environment and attitude with coefficients of 0.402 and 0.327, which were positively correlated with math achievement Y2. The contribution rate affecting English achievement is in the order of sports hygiene, sports attitude and exercise habit, and their coefficients are 0.398, 0.295 and 0.124, all of which are positively correlated with English achievement Y3.
Shaanxi Red Cultural Resources into Party Building Education in Colleges and Universities under New Media Environment
Authors: Duan Zhaohui
Published: February 2024

Under the new media environment, exploring how Shaanxi red cultural resources can be integrated into party building education in colleges and universities significantly improves students’ political literacy and cultural cognition. The study adopts new media technology, combines K-means clustering algorithm and fuzzy cluster analysis method to extract students’ personalized characteristics, and uses them to optimize the content of party building education. The experimental data show that the intra-class average error of the K-means algorithm in the classification of excellent students is 2.56, while the overall average intra-class mistake of the fuzzy clustering algorithm is 10.28, indicating that the latter is more scientific and accurate in party assessment. Through the research and analysis of the student groups in 30 universities, it was found that 80% of the students believed that the universities paid attention to the development of intelligent party building, and 0.9198 of the students thought that the red cultural resources in Shaanxi could meet the needs of party building education. The study’s conclusion shows that combining technical means in the new media environment and Shaanxi red cultural resources can effectively improve the effect of party building education work in colleges and universities, and enhance students’ political literacy and cultural cognition.
Research on the Teacher Team Construction of Innovation and Entrepreneurship Education in Higher Vocational Colleges and Universities under the Background of “Double Creation” Upgrading
Authors: Hu Fenglin
Published: February 2024

With the promotion of “dual-creation” education, higher vocational colleges and universities face the challenge of building faculty. This study explores the strategies for making the faculty of innovation and entrepreneurship education in the context of “dual-creation”. The study adopts the evaluation model based on Backpropagation neural network. It proposes targeted construction strategies by analyzing the teaching process, teacher conditions and teaching effect of innovation and entrepreneurship courses. The results show that by optimizing the construction path of the faculty team, the teaching effect of innovation and entrepreneurship courses is significantly improved, the proportion of students participating in entrepreneurial practice has increased by 40%, and the number of declared innovation patents has increased by 47.6%. In addition, the percentage of rejuvenation of the teacher team grew by 10%, and the professional ability of teachers was significantly improved. Artificial intelligence technology such as neural network optimizes the path of teacher team construction for innovation and entrepreneurship education in higher vocational colleges and universities, which can effectively improve the quality of teaching and the professional ability of the teacher team.
Research and application of art design based on visual communication technology
Authors: Yuan Lei , Huang Peng , Luo Wei
Published: February 2024

This paper deeply discusses the art design based on visual communication technology, studies its application in modern art expression, and its influence and promotion on design aesthetics. The research starts from three aspects: the hierarchical needs of art design, the relationship between visual communication and art design expression and the integration path of art design and visual communication. Through the hierarchical analysis of art design expression content, the application of media design, composition design and metaphor design in visual art is discussed in depth. Meanwhile, the relationship between art design and visual communication is studied and the path of integration between art design and visual communication is proposed. The experimental part includes the extraction of art design style features, the recognition of artworks based on color style and texture features, and the satisfaction analysis of art design effects. The results show that the method proposed in this study has high accuracy in art style recognition, excellent performance in art texture recognition, and good feedback regarding satisfaction with art design effects. This proves the effective application of visual communication technology in art design, which is significant in promoting modern art design development.
Digital Technology Boosts the Construction of English Smart Classroom in Colleges and Universities
Authors: Jiang Lei
Published: February 2024

The application of digital technology in English smart classrooms in colleges and universities explores its impact on teaching effectiveness and students’ academic performance. Based on digital technology, this study examines the construction of English smart classrooms in colleges and universities and its impact on teaching effectiveness. The study collects and analyzes 590,000 teaching behavior data, uses a clustering model to explore teachers’ teaching behaviors, and constructs a set of algorithmic overlay methods for evaluating students’ performance. Through the six-month competent classroom teaching practice, the experimental group showed significant improvement in thinking ability, learning effect and achievement. Specifically, the experimental group’s mean values of verbal thinking, creativity, and critical thinking dimensions were higher than those of the control group by 0.663, 0.572, and 0.581, respectively, and the synergistic ability and problem solving ability were higher by 8.96% and 15.59%, respectively. In addition, the experimental group’s learning attitude, process participation and knowledge mastery were significantly improved, and their academic performance improved by 5.941 points during the experiment. These data confirm the effectiveness of the intelligent classroom in improving learning effect and achievement.
Analysis of Influential Elements and Performance Evaluation of the Development of New Business Subjects in the Perspective of Rural Revitalization
Authors: Kang Jianlin
Published: February 2024

This study deeply explores the development influencing factors and performance evaluation of new agricultural management subjects under the perspective of rural revitalization, provides data support through empirical analysis, and provides guidance for promoting rural economic development and optimizing agrarian management system. A performance evaluation model containing four dimensions of technology, finance, market and society is constructed using the hierarchical analysis method (AHP), multiplicative exponential weights (MEW) and entropy value method. The study results show that among the technical indicators, the member learning ability scores the highest (0.06575), while among the social indicators, the social responsibility and radiation ability scores are the same (both 0.087). Among the financial indicators, operational capacity scored the highest (0.07025) and customer acquisition rate scored the highest (0.07575) among the market indicators. The comprehensive evaluation shows that the technical indicators have the most significant impact on the development performance of new agricultural business entities. This study provides a quantitative analysis of the development of new farm business subjects, which is effective for promoting the rural revitalization strategy.
Strategic innovation research on “dual-carbon” goals and high-quality development of urban green economy in the context of information technology
Authors: Lai Along
Published: February 2024

In information technology, realizing the goal of “dual carbon” and the high-quality development of urban green economy is an essential challenge for contemporary society. This paper explores how to effectively realize this goal with the impetus of technological progress through strategic innovation research. An improved K-means algorithm is used to cluster analyze the relationship between urban economic development and the “dual-carbon” goal. The Study firstly outlines the K-means clustering algorithm, and then proposes an improved algorithm, DT-Kmeans. through the empirical Analysis of cities in central China, this Study finds that the DT-Kmeans algorithm can more accurately analyze the contribution of the city’s dimensions to the economic development. Specific results show that the contributions of the five dimensions of innovation, coordination, green, openness and sharing are 23.76%, 5.72%, 30.49%, 15.21% and 24.85%, respectively. This indicates that the green dimension impacts urban economic development in the context of “dual carbon”. In addition, the Analysis of new energy use in central China found that the installed capacity of clean energy increased from about 4 million kilowatts in 2015 to more than 8 million kilowatts in 2020, an increase of more than 100%. Through technological innovation and strategy adjustment, the city can simultaneously realize the “double carbon” goal and promote high-quality economic development.
The Construction of “Interactive” English Translation Teaching Mode in the Background of Internet
Authors: Li Xiaoming
Published: February 2024

This paper discusses the construction of “interactive” English translation teaching mode in the background of the Internet, and analyzes the application of neural machine translation model in improving the effect of translation teaching. The research adopts Word2vec algorithm to train word vectors, combines neural network language model and encoder-decoder structure, and constructs Transformer model and CNN+Transformer model to improve translation efficiency and quality. The translation experiments on sentences of different lengths show that the models perform better in short sentence translation. The experimental results show that the proposed model outperforms the traditional model in terms of BLEU value, and the best translation effect is found in test set 1, where the BLEU value is improved by 10.6 and 11.7 compared with that of the baseline model and the CNN model, respectively. The “interactive” teaching mode designed with POA theory significantly outperforms the traditional teaching mode regarding students’ lexical, grammatical and semantic translation quality. The “interactive” English translation teaching with neural translation model can effectively improve the quality of translation teaching and students’ learning effect.