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To deeply analyze the performance of paper-cutting elements in interior space design, this paper explores the artistic expression of paper-cutting elements. The BIM model is constructed based on the TVL algorithm, and a noise-reducing style migration method with an additional total variable loss function is used to fuse the texture features of a specific paper-cutting style so that the paper-cutting texture is generated in the target object area more intensively. The algorithm is supplemented with a U-shaped network structure to obtain the mask map of the target object, modify and expand the ScanNet training data set, and input a content map and a style map to generate the stylized paper cutout image automatically. The results show that compared with other elements, the stability of paper-cut elements reaches 96.31%, the image smoothness reaches 89.34%, and the retention of significant regions reaches 93.26%. In modern design, the peak signal-noise of paper-cut elements improved by 13.418d on average, the structural similarity also improved by 0.34807, and the recognition rate of paper-cut elements in the furnishing category reached 95%. Applying paper-cut art in interior space design can give interior furnishing design better visual effects, provide inspiration and ideas for interior space design innovation, and contribute to improving aesthetics.

The study of metadiscourse word resources is an important way to reveal academic knowledge constructs and genre features in English papers. This paper selects intra- and interdisciplinary English academic papers as data samples, and the text representation is based on the vector space model. The text similarity is calculated using the word movement distance model to complete the clustering based on the density peak, and then the semantic annotation of metadiscourse verb patterns is performed using the conditional random field. Then, the intra- and interdisciplinary metadiscourse annotations are compared to analyze the variation of verb patterns. The intra-disciplinary variation of metadiscourse verb patterns was mainly found using attitude markers, where the log-likelihood ratios of 48.56, 30.25, and 35.64 were obtained for affirmative, critical, and critical declarative attitudes, respectively. In interdisciplinary studies, basic disciplines preferred to use verb patterns as transitions, while applied disciplines preferred to express the author’s point of view. The study of metadiscourse verb patterns effectively enriches the linguistic resources in academic English teaching and deepens the understanding of the characteristics of disciplinary essay genres.

With the help of the dynamics model of the integration of AI and the management of college civic education, this paper investigates the integration of artificial intelligence and civic education in three directions: application specification, application design, and application practice. Using differential equations to represent the non-linear dynamics equations of the system and by means of differential solutions, the evolution mechanism of AI education application and integration is projected in this paper. The integration of AI and college civic education is explored through empirical analysis and comparative analysis. The results show that the average value of the comprehensive score of the three directions in the sample colleges and universities is 77.5828, which is in the bare integration stage, and the integration degree in the “formulation” path is the best, which is 85.698, and is in the complete integration stage. The students in the experimental class of the sample universities scored better than the control class without AI integration in civic education. There were no students with scores below 60, and there were 32 more students than the control class in the 90–100 segment.

Labor education in colleges and universities in the new era is the fundamental task of implementing the principle of establishing morality and educating people, as well as an essential hand in cultivating college students with all-round development of morality, intelligence, physicality, aesthetics, and labor. This paper focuses on the objective evaluation method of college students’ labor education to conduct research. Using the improved binary tree support vector machine multi-class classification algorithm, an evaluation model for college labor education based on a binary tree support vector machine has been constructed, and an evaluation system for labor education has been developed. The results show that the majors that have compulsory labor education courses, elective labor education courses, and have added labor education modules in their major courses are all art and sports, accounting for more than 50% on average. The percentages of liberal arts, science, economics and management, art and sports, medicine and science, and agriculture, which did not offer labor education-related courses, were 26.35%, 22.16%, 19.91%, 13.59%, 11.03%, and 13.19%, respectively. It is evident that labor education courses are not distributed equally in colleges and universities, and there are still some majors that do not offer labor education courses. In terms of labor education, most of the labor education through Civics and Political Science classes is provided to students. Managers are those who choose the most specialized courses. Students also tend to participate in bicultural education and social practice activities, accounting for 29.02% and 43.01%, respectively. It shows that the algorithm in this paper can objectively and effectively evaluate labor education in colleges and universities and identify the deficiencies in education.

With the advancement of the information age, the traditional teacher-led English classroom interaction in colleges and universities has made it challenging to meet the individualized needs of different students. Therefore, this paper takes the current classroom behavior data of college students as the basis, uses the K-means clustering algorithm and I-divergence measure to categorize, analyze, and study the student behavior data, and digs out the correlation between students’ classroom behavior data and teaching interaction. This paper takes all the teachers and students of English majors in college M as the research samples. It conducts experimental research in terms of teachers’ and students’ classroom speech rates, teachers’ and students’ questioning rates, and teachers’ and students’ use of instructional technology, etc. The 10 classes of English majors in college M do not have more than 50% of the students’ speech rate, except for class 2. Class 2 has the highest speech rate (61.64%) among students, while class 5 has the lowest rate (7.5%). From the point of view of active questioning rate, 10 classes had very low SQ ≤ 0.02, and even in classes 1, 3, 4, 5, and 6, there was a situation where students’ active questioning rate was zero. After analyzing the student behavior data in some classes, it was found that the mean score of English test students in the experimental group before and after the test increased by 4.687, corresponding to a significance Sig value of 0.000

This paper combines the actual situation of security management in colleges and universities, and analyzes the demand for security innovative management platform in colleges and universities to facilitate teachers and students to live happily and research in a comfortable and modernized campus. According to the different functionality, the innovative campus system can be divided into the 3D campus roaming navigation module based on VR technology, the intelligent attendance module based on the combination of convolutional neural network and face recognition, and the induced parking module based on ant colony algorithm. The structure of each module and the operation process are all described in detail. Simulation experiment analysis is used empirically to analyze the intelligent campus management platform. The results show that the method of this paper has an average pass rate of about 89.55% for a given number of vehicles, and compared with the pre-optimization period, the waiting time for parking has been reduced by 19.5%, with an optimization rate range of 72.37%. Even after the optimization of the method in this paper can not wholly avoid the parking wait, but it can reduce the parking wait time, which for the parking lot, can ease the parking congestion in the peak time. The theoretical guidance provided by this study is used to construct a more brilliant campus, promoting the development of security and intelligent management in colleges and universities.

This paper constructs an assessment framework through theoretical research and literature study, determines the assessment indicators using the systematic review method, and forms a final assessment system of social resilience after flooding based on the correlation test. On this basis, the index weights were determined using the comprehensive assignment method, and the social resilience index was calculated using multi-criteria optimization and compromise solutions methods. The spatiotemporal evolution trend and spatial correlation characteristics of post-flood social resilience were explored by combining mathematical statistical methods, geographic information technology, and spatial measurement models. The temporal evolution characteristics of different dimensions of social resilience during the period 2012–2021 show that the social resilience index declined steeply in 2012, reached a maximum value of 0.316 in 2018, then gradually decreased to 0.305 in 2020, and recovered to 0.312 in 2021, with an overall increase of 5.05%. Assessing the social resilience of 17 cities under the jurisdiction of Henan Province, overall, the ability of the social system to cope with heavy rainfall and flooding disasters in 10 cities showed an increasing trend, and 7 towns showed a decreasing trend.The social resilience of Zhengzhou City under heavy rainfall and flooding disasters in 2017–2021 showed a trend of decreasing and increasing, with a composite index fit of 0.885, 0.893, 0.806, 0.893, 0.806, 0.824, 0.861.This paper constructs a social resilience assessment system after flooding to provide some reference for central cities to cope with extreme weather.

Here argued that human behavior is primarily governed by habit, not by biological drive. When the need is satisfied, the drive generated by the need will decrease. This process is also the stimulation and reinforcement of the connection between the problem-coping behaviors, that is, the enhancement of the strength of the habit. This paper applies Herner’s drive theory to explore the influencing factors of college students’ fire awareness to strengthen college students’ behavioral potential in the face of fire danger. Based on the current situation of fire education, this paper analyzes the three influencing factors of the driving force, inducement, and habit intensity through the theory of Hull drive reduction, and discusses how to strengthen the habit intensity of college students in the face of fire danger and strengthen the behavioral connection between fire risk and safety countermeasures, which is conducive to exploring the characteristic fire safety education mode of colleges and universities.

With the rise of foreign trade, various industries have increased the demand for foreign language talents, and colleges and universities have paid more attention to the intelligent teaching of business English. Based on this, this paper proposes a dynamic time regularization recognition algorithm (DTW). It improves the recognition efficiency of the algorithm, preprocesses the speech signal, and selects the feature parameter MFCC. The improved DTW algorithm is used to construct an intelligent mode of mixed teaching of business English in colleges and universities, and the learning effect of the students majoring in business English is calculated and evaluated. The research object of this paper is students majoring in business English at college C and is conducted by conducting experimental research on their oral learning, article writing ability, and learning attitude. From the results of oral teaching, the coefficient of variation P-values before and after the application of each group are 0.000, 0.002 and 0.000 respectively, which are all less than 0.05, indicating that students with different English fundamentals have a more significant effect of improving their performance after applying the mixed teaching mode. Before the application, the average score for article writing increased from 59.67 to 64.50. In terms of affective attitude and value, the learning interest risen from 2.75 to 2.97, with the most significant improvement Thus, it can be seen that the intelligent hybrid teaching mode constructed in this paper can effectively improve the teaching effect of business English in colleges and universities.

Strong winds, typhoons, and other extreme weather can cause significant damage to house structures, resulting in great harm to human life and property safety. It is imperative to prepare and strengthen infrastructure against these extreme weather conditions to minimize their impact. This paper conducts an on-site survey of low-rise houses in Cangnan County and Dongtou District, located on the south coast of Zhejiang, affected by typhoons. It summarizes the structural characteristics of houses and the damage caused by typhoons to house structures. The paper also performs a preliminary analysis of the damage mechanism of houses. Additionally, it examines wind-resistant measures used by the private sector, identifies potential issues with wind-resistant design, and proposes corresponding wind-resistant countermeasures and recommendations for low-rise houses. The research presented in this paper can be utilized for analyzing the wind damage mechanisms of low-rise houses in the future. Moreover, it offers a valuable reference for the analysis and simulation of wind damage in low-rise houses in typhoon-prone coastal areas. This research holds significant value in preventing wind damage to low-rise houses.

In the contemporary educational environment, the protection and inheritance of traditional music culture faces challenges. The current diverse needs of music education urgently require innovative teaching models to integrate traditional music culture and enhance students’ aesthetic and cultural literacy. The eco-beauty perspective provides a new educational idea that emphasizes the harmonious coexistence with nature and promotes all-around development of students. Combining traditional music culture and modern education technology effectively can be achieved by combining deep learning technology and new paths in music education. This study explores the music teaching model that integrates traditional music culture with deep learning technology, under the perspective of ecological aesthetic education. The study utilizes a mixed methodology, combines quantitative and qualitative Analysis, and compares the experimental design to verify the effectiveness of the teaching model. In this paper, implementing the music teaching model under the perspective of ecological aesthetic education has improved students’ performance in music perception ability, music emotion expression, music composition and performance, and music knowledge theory. The average increase in music perception ability of the experimental class students is 5.18 points, and the average increase in music composition and performance ability is 11.57 points, showing the impact of this teaching mode on the overall improvement of students’ music ability. In this paper, the music teaching mode combining deep learning under the perspective of ecological aesthetic education can effectively integrate traditional music culture, promote the improvement of students’ music literacy, and provide a new teaching strategy for the inheritance and development of traditional music culture.

To solve the problems of fuzziness, nonlinearity and complexity of economic factors and environmental factors in the evaluation of ecological economic benefits of enterprises are studied. Based on the theory of environmental-economic benefits (EEB), this paper defines the value of environmental resources and economic benefits of enterprises, and puts forward the indicators of environmental and economic benefits. The evaluation model of enterprise EEB based on the analytic hierarchy process and fuzzy set algorithm is constructed. By establishing the hierarchical structure model of index system, the intuitive fuzzy judgement matrix is obtained, and the final score of enterprise environmental benefits is calculated by consistency test and weight analysis. After verifying the model, the evaluation method of enterprise EEB proposed in this paper is accurate and reliable, which is helpful to deeply analyse the hidden order and adaptation mechanism of complex adaptation system of enterprise environmental management.

In order to conform to the development of the times and effectively promote the reform of vocal music education in colleges and universities, this paper proposes an innovative path of diversified vocal music education reform in colleges and universities under the modern advanced educational concepts that are steadily gaining utilisation and popular acceptance in China and the West. In this paper, the passive collective behaviour of biology is introduced in the teacher stage in the basic TLBO algorithm, and then the feedback stage is added after the student stage to increase the effectiveness of students’ learning methods, maintain the diversity of students and make available a system the deployment of which would swiftly bring students with poor grades to the attention of teachers; to this end, the algorithm is made to perform a local fine search, and the optimisation accuracy of the algorithm and its global search ability are improved. Finally, based on the numerical simulation of the standard test set and compared with the existing optimised TLOB algorithm, it is concluded that 92% of the solutions in the ETLBO are dominated or not dominated by the solutions in the non-dominated solution set of the optimised TLBO, while only 2% of the solutions in the non-dominated solution set of the optimised ETLBO are dominated by the non-dominated solutions of the TLBO. Centralised solutions dominate or do not dominate each other. It is concluded that the optimised TLBO algorithm has the best solution ability and also has practical application significance.

In digital rural governance, the effective integration of accounting and rural development is crucial for maximizing rural economic benefits. This paper uses CiteSpace software and LDA modeling to examine the current state and hot topics in rural accounting research from 2018 to 2022. The study was visualized by analyzing valid literature and using perplexity to determine the best effect of topic extraction. The results show that the number of annual publications in this field continues to grow, and the number of universities and institutions publishing relevant papers is also increasing yearly. Regarding research hotspots, the keyword “rural” appears most frequently, reaching 122 times, followed by “financial management” with a frequency of 106 times. The keyword with the highest centrality is “rural revitalization”, with a centrality as high as 0.38. The research keywords are mainly divided into rural revitalization and financial management, with the financial management group being the most dominant. However, the number of studies on rural revitalization has been increasing yearly, and the gap between them has been narrowing. The importance of accounting is growing in promoting rural revitalization and improving rural economic efficiency.

There is a close relationship between abnormal lipid metabolism and tumor malignant behavior, and abnormal lipid metabolism plays a crucial role in developing malignant tumors. Based on BiLSTM model, combined with Transformer Encoder and CRF decoder, the article successfully constructed a joint extraction model of medical literature, and with the help of Neo4j technology, built the knowledge graph of lipid metabolism abnormality and tumor occurrence and development. The study used data sources from WOSCC, CNKI, Wanfang, VIP, and other databases as data sources. It used bibliometric analysis and tools like CiteSpace and VOS viewer to visualize and analyze related studies in-depth. The study results showed that Oral Oncology in the UK ranked first in abnormalities of lipid metabolism in the number of publications, with a total of 301 related papers and an impact factor of 5.118. The number of studies related to oncology increased from 11 in 2010 to 414 in 2023, with a growth of more than 36.64 times. By comprehensively analyzing the current situation of abnormal lipid metabolism and tumorigenesis and development, this paper provides new ideas and perspectives for the study of cancer prevention, diagnosis, treatment and mechanism. It helps to promote the in-depth research and development of related fields.

In-depth research on landscape art interrelating with interior and exterior space design is still relatively limited. In this paper, we use gray correlation analysis to investigate the connection between landscape art and indoor and outdoor space design to better understand and apply the potential of landscape art in design. In this paper, we investigate the application of landscape art in indoor and outdoor space design, including the Use of landscape gardening techniques and landscape formation types. The study constructed an evaluation index system, including 16 indicators for road landscape, site landscape, green landscape and other types of landscapes, which were screened and weighted according to systematicity, operability, participation, dynamism and the combination of qualitative and quantitative indicators. We used AHP to determine the indicators’ weights and gray correlation analysis to calculate comprehensive scores for different design solutions. The best indoor and outdoor design solutions were determined by comparing and ranking the evaluations of various design solutions through case studies and statistical analysis. The method facilitates the scientific assessment and selection of indoor and outdoor design solutions to enhance the quality of design and the comfort of the human environment.

Since the birth of Bitcoin, blockchain technology has been widely used in many fields, such as finance, supply chain management, smart contracts, etc.and has been highly valued for its decentralized and tamper-proof characteristics. With the expansion of application scenarios, blockchain faces inefficient data processing challenges, particularly in event sequencing and processing. Developing efficient event sequencing algorithms is a research priority to enhance the performance of blockchain systems. This study addresses the efficiency problem in blockchain event sequencing by introducing a high-dimensional multi-objective differential evolution strategy to improve blockchain technology’s performance in various fields. This paper uses the NSGA-III-WA algorithm to efficiently sort blockchain events by combining differential evolution strategies. Compared to the traditional sorting algorithm, this algorithm reduces latency by 30% and improves memory usage by about 20% when dealing with events of the same size, ensuring sorting accuracy. By optimizing the weight vector adjustment strategy, the convergence speed and distribution uniformity of the algorithm and the solution can be optimized. In this paper, the algorithm can effectively improve the efficiency and performance of blockchain event processing, which is critical in optimizing blockchain systems.

Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

Global carbon emissions and climate change are of increasing concern. As a critical indicator of the carbon efficiency of economic activities, carbon productivity has improved, resulting in a relative reduction in carbon emissions and output growth. As an essential part of the national economy, foreign trade profoundly impacts carbon productivity by promoting technology exchange, industrial upgrading, and structural adjustment. The carbon emission impact of foreign trade is not uniform, and there are noticeable differences between countries and regions. This paper examines how foreign trade affects carbon productivity and through which mechanisms, and further analyzes regional and industrial differences in these effects to provide a basis for the formulation of relevant policies. According to the study, there is a significant time lag effect on foreign trade’s impact on carbon productivity, and there are differences between industries and regions. Promoting foreign trade in this paper results in technological progress and improved carbon productivity, with an average improvement of 5.2%. By optimizing and adjusting industrial structure, carbon productivity is promoted better, with an increase of around 4.3%. High carbon emission industries have a relatively weak positive effect. Foreign trade positively impacts carbon productivity by promoting technological progress and industrial restructuring, but it is necessary to have differentiated policies for different industries and regions.

This paper studies and analyzes the theoretical value of the college English education course Civics based on fuzzy set theory and explores the optimization path of its teaching mode. The article uses literature analysis, case studies, and multiple linear regression models. In colleges and universities, Civics and Politics curriculum is instrumental in promoting excellent culture and enhancing students’ comprehensive quality and moral cultivation. Implementing the WITS model makes the teaching process more efficient and encourages students’ active participation and deep thinking. The students ‘English reading level significantly improved after integrating the elements of civics and politics into the teaching process. After analyzing 220 questionnaires, it was found that factors such as course content and communication environment in the teaching process significantly impacted the teaching effect, in which teaching activities and course themes related to Civics and Politics made the most significant contribution to the teaching effect. The research in this paper proposes suggestions for optimizing teaching, such as improving teachers’ civic and political literacy and innovating teaching contents and methods. After studying and analyzing the curriculum, Civic and Political has an essential theoretical and practical value in English education in colleges and universities. Optimizing the teaching mode can improve students’ English proficiency and ideological and political quality.

This paper aims to explore the integration of Marxism and Chinese-style modernization and its practical application in the context of the digital intelligence era. Through theoretical Analysis and empirical research, a system for assessing development of Marxist Chinese-style modernization has been established. The study finds that Marxism’s multiple characteristics, such as political, scientific, people’s, practical, developmental, and revolutionary, provide theoretical support for Chinese-style modernization. This paper uses grey correlation analysis and multiple linear regression models to evaluate the modernization development of 31 provinces, municipalities, and autonomous regions in China during 2010-2019. The study results show that the Marxist Chinese-style modernization index has grown from 0.164 in 2010 to 0.731 in 2020, with an average annual growth rate of 34.57%. The index significantly impacts the quality of the regional economy, cultural industry, and ecological environment. The study shows that the application of Marxism in China accelerates economic development and promotes the governance of cultural industry and ecological environment, which provides impetus for the construction of socialist modernization with Chinese characteristics.

Exploring the relationship between students’ study habits and foreign language performance is of great significance to the field of education, this paper investigates the correlation between students’ study habits and foreign language performance, and uses Bayesian networks to construct a labeled correlation model of study habits and their effects on foreign language performance. A multi-labeled Bayesian network model was built to assess the impact of different study habits on foreign language achievement by collecting and analyzing data on study habits and foreign language achievement of 110 college and university students. In this study, there was a significant positive correlation between the characteristics of learning behaviors in class (e.g., pre-study and review) and foreign language achievement, with correlation coefficients of 0.366 and 0.411, respectively, while the association between the characteristics of life behaviors (e.g., sleep and diet) and foreign language achievement was relatively weak. The study also showed a high positive correlation (0.283) between positive attitudes towards school and foreign language achievement. Through Bayesian network analysis, this paper further confirms the importance of good study habits in improving academic performance in foreign languages. By optimizing students’ study habits and extracurricular learning behaviors, the study can effectively enhance foreign language learning outcomes.

Digitalization is a fundamentally disruptive force triggered by the fourth Industrial Revolution and the Internet of Things, driving technology upgrades, so what impact does digitalization have on the environment? In this paper, DAG-SVAR model was constructed to verify it. Through impulse response analysis, it was found that digitization drives technological upgrades, and environmental pollution is reduced through intelligent production, improved information environment, and ameliorated governance level. This paper suggests that, on the one hand, the technology upgrade is the key to digital empowerment in new fields and new spaces. Enterprises need to constantly explore diversified application scenarios, help the digital and intelligent transformation of energy, transportation, manufacturing and other industries, and greatly promote the integration and interaction of technology and various fields and sustainable development; on the other hand, it is necessary to strengthen digital ecological governance, promote the interconnection of everything, human-computer interaction, and the integration of space and land, so that information resources can fully flow, to create a digital green ecology.

Climate change is triggering more frequent and intense hydrological disasters, which significantly impact society and economy. This paper discusses the specific impacts of these changes on hydrological hazards. It analyzes the early warning mechanism, risk assessment, and coping strategies to provide a scientific basis for effective response. This paper aims to explore the impact of climate change on hydrological disasters and develop effective prediction and response strategies. The effects of climate change on the mechanisms of hydrological disasters, risk assessment theory, influencing factors, and early warning mechanisms is studied through a comprehensive analysis method. According to the study, climate change significantly impacts the frequency and intensity of hydrological disasters. In a particular region, there has been a 30% increase in the frequency of floods caused by heavy rainfall over the past 20 years, leading to a 40% increase in economic losses. In addition, climate model-based risk assessment methods effectively predicted the potential impacts of these disasters. The study revealed that enhancing early warning systems and raising public awareness can reduce catastrophe risk. Climate change significantly impacts hydrological hazards, and more systematic and integrated management strategies are needed to reduce their impacts.

In the evolving landscape of higher education, integrating information technology into Wushu teaching management presents an opportunity to elevate teaching standards. This paper investigates the development of an innovative platform for Wushu instruction, utilizing cutting-edge video analysis techniques to enhance learning outcomes. By analyzing video data from Hikvision DS-2CD2051-1 webcams with advanced preprocessing methods, we demonstrate the effectiveness of the Single Shot MultiBox Detector (SSD) algorithm in improving action recognition over conventional CNN approaches. Our results reveal a notable enhancement in the quality of Wushu movement evaluation, evidenced by a 72.02% accuracy rate and a 0.3865 Mean Squared Error (MSE). The deployment of this technology in a university setting not only fosters a significant uplift in student performance but also boosts their engagement and satisfaction levels. This paper contributes to the discourse on educational technology by offering a successful model for the digital Transformation of Wushu teaching in academic institutions.

The rapid development of digital technology significantly impacts the use of cell phone interfaces by the elderly. This paper aims to analyze the interaction design of cell phone interfaces for the elderly to enhance their user experience and ease of use. This paper investigates the evaluation system for evaluating cell phone interface interaction for the elderly, including interface size, Layout, and functionality. A group of elderly people aged 65-75 are invited to evaluate the effects of different design options by observing their performance under different interface sizes (large size 6.0 inches, medium size 5.5 inches, small size 4.5 inches) and layouts (nine-cell grid and combination type). The evaluation metrics are task completion time, number of operation errors, and eye movement data. The 6.0-inch interface experienced an average task completion time of 6.97 seconds, a decrease of approximately 6 seconds and 1 second from the 4.5-inch and 5.5-inch interfaces, respectively. The large-size interface had an average error rate of 1.11 per task, significantly less than the average error rate in the 4.5-inch and 5.5-inch interfaces. The more extensive interface significantly decreased user gaze points and gaze time, indicating more efficient information acquisition. More extensive cell phone interfaces and combined layouts can significantly improve interaction efficiency and reduce operation errors for elderly users, enhancing user experience. The findings of this study offer practical guidance for designing cell phone interfaces for the elderly and aid in promoting cell phone interface design for the elderly.

This paper analyzes the effectiveness of predictive analysis of brain imaging data based on deep learning algorithms, and improves the prediction accuracy and efficiency of brain imaging data through improved methods. The first step is to measure the local consistency of the brain imaging data using Kendall’s concordance coefficient (KCC), and to analyze the differences between the datasets using the two-sample t-test. Secondly, a batch normalized convolutional neural network (BN-CNN)-based prediction method for brain imaging data has been developed. This method extracts spatial and temporal features in two convolutional layers, followed by a fully connected layer for classification. Experimental results show that this method is helpful in predicting missing structural data in brain imaging. Secondly, a batch normalized convolutional neural network (BN-CNN) based brain imaging data prediction method is developed, which extracts spatial and temporal features in two convolutional layers. Then it connects to a fully connected layer for classification. The experimental results show that this method’s structural similarity index (SSIM) and feature similarity index (FSIM) in brain imaging data prediction of missing data reaches 0.9446 and 0.9465, respectively, which is significantly better than that of other GAN benchmarks. In applying the method to epilepsy and Parkinson’s cases, this algorithm is used to epilepsy and Parkinson’s cases, and a two-sample t-test analyzes the differences in the data sets. In the application of epilepsy and Parkinson’s cases, the algorithm in this paper has an average prediction accuracy of 93.37%, effectively reducing the rate of incorrect predictions. Deep learning algorithms are highly efficient and accurate in predicting brain imaging data, which is crucial for future clinical diagnosis and treatment.

This paper investigates the application of exploring spatial clustering techniques in travel recommendation systems to improve recommendation accuracy and user satisfaction. A system that can provide personalized travel information has been designed by analyzing the spatial data and behavioral patterns of travelers. With this approach, the travel experience can be optimized by considering geographic location, user preferences, and tourist attraction characteristics. User data and attraction characteristics are analyzed in this paper to obtain accurate travel recommendations. Combining the traveler’s interest matrix and the attraction area heat algorithm is used to optimize the recommendation process. This recommender system reduces the root mean square error (RMSE) and mean absolute error (MAE) by 0.487% and 21-60%, respectively, compared with the traditional algorithm in the travel recommendation error analysis, and improves the accuracy rate to 96.33%, with a significant increase in the recall rate. The spatial clustering cluster analysis demonstrates that the chosen number of clusters can effectively enhance the clustering quality and maximize the recommendation results. By effectively utilizing spatial data of users and attractions, the system can provide personalized travel recommendations and improve user experience with high accuracy and recall, demonstrating better recommendation performance than traditional methods.

This paper investigates music education, where an efficient and accurate performance evaluation system in the piano teaching and performance ecosystem is increasingly becoming an essential tool for improving teaching quality and performance level. The objective evaluation of students’ performance skills can be achieved by carefully analyzing piano performances using the network flow optimization technique. This technique optimizes the performance evaluation system’s audio recognition ability by analyzing the piano audio signal and solving the multi-constraint nonlinear optimization problem in a limited time domain. This paper establishes a network flow optimization model, applies the multi-constraint nonlinear optimization technique, and combines the non-negative matrix decomposition and dynamic time regularization algorithm to analyze the piano performance for experiments. After optimization processing, hundreds of piano audio samples were collected, and the audio recognition accuracy was improved by 20%. By optimizing and processing the audio signals from the network stream, the evaluation system could detect polyphony more accurately and track the musical score effectively, improving accuracy and efficiency. Using the non-negative matrix decomposition algorithm, the accuracy of detecting polyphony can reach 85%, while the dynamic temporal regularization algorithm can match the position of the musical score with 95% accuracy. The accuracy of piano performance evaluation is optimized by this network flow optimization method, providing new technical means for music education, and promoting the quality of teaching and performance.

The development of artificial intelligence technology has brought unprecedented challenges and opportunities for higher education management and student training mechanisms. The traditional education management mode and student training strategy appear to be incompetent in the face of new technology and urgently need innovation and reform. In the face of the latest trend of education informatization, higher education institutions must explore management and cultivation means that keep pace with the times, and take artificial intelligence as the driving force to create a new system of intelligent education to meet the diversified needs of education in the digital era. This paper analyzes the design and functions of the smart education platform, as well as its practical application effect in education management and student training. By introducing the intelligent education platform with artificial intelligence, the utilization efficiency of teaching resources was improved by 20%, and student satisfaction increased by 30%. Based on data analysis, this paper thoroughly discusses the effectiveness of the intelligent education platform in improving the quality of education and optimizing the allocation of educational resources. The modernization of higher education management and comprehensive improvement of students’ abilities can be effectively promoted by incorporating artificial intelligence technology in this study, which provides new ideas for building future education.

In this paper, we firstly explore the existence of solutions to the following linear Schrödinger Kirchhoff Poisson equation with critical exponential growth on the full space □3 by using the discontinuous finite element (DG) as well as the principle of centralized compactness: {−(a+b∫□3|∇u|2)Δu+V(x)u+φu-12uΔ(u2)=K(x)up−2u,x∈□3−Δφ=u2,x∈□3\left\{ {\matrix{{ - \left( {a + b\int_{{\square^3}} {{{\left| {\nabla u} \right|}^2}} } \right)\Delta u + V\left( x \right)u + \phi u - {1 \over 2}u\Delta \left( {{u^2}} \right) = K\left( x \right){u^{p - 2}}u,} & {x \in {\square^3}} \cr { - \Delta \phi = {u^2},} & {x \in {\square^3}} \cr } } \right., x ∈. Secondly, we make reasonable assumptions on the V, K , f functions of the equation, and use the principle of variational division to firstly obtain the corresponding energy generalization of this equation, and then we prove the corresponding generalizations of the equation satisfy the (C)c conditions. Finally, the existence of the solution of the equation is obtained by numerical simulation and then by using the Yamaji Lemma. The results show that the error of the finite element solution of the linear Schrödinger Kirchhoff Poisson equation in the spatial direction P1 reaches the optimal estimation under the L2 -parameter in an intermittent finite element numerical simulation environment, i.e., it is proved that there exist at least 1 and 3 positive solutions to the problem. The paper achieves rich research results which are informative for the solution of this class of linear differential equations.

As the living standard of the people in China has changed drastically, the aesthetic quality of daily life has been increasing day by day, and people’s desire for a better life is also increasing. This paper proposes a color harmony model based on hue statistical features, using maximum likelihood estimation to extract the main hue region and extract related features from the corresponding region. Based on the inconsistencies in feature dimensions, a model is created to evaluate color harmony using Gaussian mixture training and is employed for image aesthetics evaluation. Finally, regression is employed to analyze the factors that influence life aesthetics evaluation, and the model is tested and analyzed using linear diagnosis and residual analysis. The results show that the influence index affecting the evaluation of daily life aesthetics is judgment beauty, and its influence coefficient is 4.4323. The product of image entropy and image gradient is used as the connotation of judging the aesthetics of daily human life, which is consistent with the evaluation of human aesthetic judgment. The theoretical connotations of life aesthetics are clarified and enhanced in this paper, and it develops its thinking to construct the reality of the good life.

This paper explores new approaches to the application and creation of digital media in the context of the rapid development of artificial intelligence technology, with a particular focus on virtual reality interaction. The study utilizes the spatial distance of the enclosing spheres in the hybrid hierarchical enclosing box and the characteristic of non-intersection between the enclosing boxes for collision detection, and the collision information returned by the rays is analyzed in depth. Meanwhile, the intersection test method is used to improve the efficiency of collision detection. And a subjective evaluation of 10 users’ experience in virtual reality art interaction was conducted. The results showed that the system was highly rated in accuracy (average score of 8.39), but rated lower in simplicity (average score of 4.28). There was a positive relationship between the user’s eye gaze duration and the number of interruptions and misoperations in art interactions. At an eye gaze duration of 0.2 seconds, half of the users did not experience interaction interruptions; when the gaze duration increased to 1.2 seconds, all users had no interruptions. This paper provides a practical reference case for using AI technology in digital media applications and creation, especially in enhancing the accuracy and efficiency of virtual reality interactions.

The teaching of vocal singing in colleges and universities needs to create a teaching environment that is both interesting and knowledgeable for students, and continuously improve their aesthetic ability and comprehensive quality. Based on the ARCS model, this paper designs the structure of the SPOC teaching platform and establishes the teaching mode of vocal singing flipped classroom based on the original folk songs in the cultural tourism industry as the main source of teaching resources. A multiple linear regression model is chosen to analyze the influencing factors of the teaching quality of vocal singing flipped classrooms, and the least squares estimation of the regression model is used to decompose the singular value. For the vocal singing flipped classroom teaching model established in this paper, a multiple linear regression model was established by choosing teaching content, teaching environment, and teaching method as explanatory variables, and the validity was verified in terms of benchmark regression and teaching practice. The results show that teaching content, teaching environment, and teaching method are all significant at a 1% level on the teaching quality of vocal singing flipped classroom, and the fitting value of the three is 0.7856, and the singing emotion expression scores of the students in the group with serious singing problems before and after the teaching experiment have been improved by 6.11 points as a whole. Enhancing students’ aesthetic abilities and emotional expression can be achieved through teaching vocal singing based on aesthetic elements.

In this paper, the cited papers and cited patents of world-class agriculture-related universities are taken as the research objects, and the preprocessing process of the research hotspot text of agriculture-related universities is accomplished by combining the participle processing method and the TF-IDF method. To make up for the shortcomings of the semantic level of the LDA model, BERTopic is utilized to obtain the document-topic distribution probability and the topic-topic word distribution probability, and the topic hotness of different topics is calculated. Then the Glove method is used to extract the word vector features of the topic words, normalize them, calculate the word vectors by taking the distribution probability values as the weighted weights, solve the topic vectors, and then calculate the similarity of the hot topics in different time windows by the cosine similarity to explore the hot topics and the evolution of the world’s first-class agriculture-related colleges and universities research.

By combining mining algorithms, this paper provides a new method for quantifying the characteristics of literary language, thereby reducing the subjectivity of the value and language discussion of ancient literature in Chinese. Firstly, the TF-IDF algorithm is improved, the methods of inter-class dispersion and intra-class dispersion are adopted, and the optimal number of topics K of the LDA topic model is determined by using the confusion degree, and the quantitative model of the value and language characteristics of ancient literary texts is constructed. Furthermore, the concept of maximum word-to-frequency ratio is introduced. It is integrated into the traditional information gain method, and an old academic text recognition algorithm based on XGBoost model is constructed. The model’s results were applied to the network corpus mining and analysis, and the results showed that the word “cherishing spring” ranked first with a frequency of 7085 occurrences, followed by “hurt autumn” with 4598 occurrences. Among the eight themes, “natural imagery” (Topic 3) accounted for the highest proportion, reaching 23.68%, followed by “landscape and pastoral” (Topic 7) and “euphemistic words” (Topic2), accounting for 16.29% and 14.54%, respectively. The method of this paper not only provides a new perspective and tool for the quantitative analysis of the linguistic characteristics of literary works, but also points out a new research direction for the in-depth discussion of textual value and linguistic characteristics in the future.

In recent years, higher education management has been improving, but the coordinated development of the overall educational management and student management system of colleges and universities still needs to be further improved and enhanced. This paper uses data envelopment analysis and data fusion technology to establish DEA fusion algorithm and DEA-Malmquist index method to evaluate and analyze students’ learning efficiency in colleges and universities. The results indicated that the average degree of perfection of online teaching management in higher education is low, 86.2%. Students’ overall satisfaction with this is also expected at 56.2%. There is a need to strengthen management and improve classroom interactivity and innovation, to enhance the quality and attractiveness of courses, and to avoid excessive fragmentation. In addition to the existing problems, the results achieved include more accessible access to learning resources, with a percentage of 88.6%. The algorithm of this paper is used to study the efficiency of higher education and student learning, and to provide a more comprehensive and objective approach to the development of the quality of higher education and student management for the later development of higher education.

With the development of the times, the cultivation of literary appreciation ability in the education system of colleges and universities has been gradually emphasized. This paper constructs a multimodal semantic analysis model based on text feature extraction, image feature extraction and audio feature extraction, and after fusing multiple modalities, combines them with sentiment semantic Analysis, optimizes them through the self-attention mechanism module, and finally constructs a sentiment semantic analysis model based on multimodal feature fusion. Through the performance analysis of the multimodal feature emotion semantic model, the algorithm model accuracy of this paper is as high as 89%, and the recognition accuracy of emotion is stable at about 80~85%. The multimodal sentiment semantic model proposed in this paper has an accuracy rate higher than 80% on literature appreciation related datasets. In the empirical evidence of literature appreciation, the average recognition accuracy rate of emotions expressed in literature is 62.80%. The study provides an achievable path for the multimodal semantic Analysis of contemporary literature appreciation integrated into college teaching, which is of practical significance for developing literature appreciation teaching in colleges and universities.

In the current aviation industry’s demand for excellent service personnel surge, this paper emphasizes the importance of physical training for college aviation service personnel to improve the image of individuals and companies. To this end, this study is oriented to the employment needs of aviation enterprises, creates the “9C cooperation” physical training and nurturing model, and establishes a detailed set of physical training quality evaluation index system. Taking University S as a case study, the data were collected through questionnaires, and factor analysis was applied to comprehensively assess the quality of training and nurturing of aviation service talents. Combined with the IPA analysis model, this paper further explores the correlation between corporate satisfaction and importance and the physical training system. The study results showed that the total variance contribution of the four main factors to the quality of physical training and nurturing was as high as 91.33%, while the overall score of the quality of training and nurturing was 3.153. The importance and satisfaction of enterprises to the quality of form training are distributed in a high range, showing that the training system needs to be further improved. Therefore, colleges and universities need to refine the teaching objectives and contents to ensure that the quality of physical training for aviation service talents can meet the actual needs of aviation enterprises and promote the industry’s overall development.

With the development of the information age, international teaching cooperation and exchanges have become increasingly frequent, and educational sovereignty and cultural security in higher education international exchanges have gradually become the focus of attention. This paper constructs a multiple regression model of educational sovereignty related to international exchanges, and designs a cultural security risk assessment model in international exchanges by combining fault tree and Bayesian network to prevent cultural invasion. Empirical Analysis is carried out on this basis to explore the factors and preventive measures affecting educational sovereignty and cultural security in higher education international exchanges. In the regression results, the standard regression coefficient of national comprehensive strength is the highest at 0.272, in which the significance of intellectual property rights, educational legislation, educational administration, state authority and national total strength are all less than 0.001. The accuracy rate is more than 90% for identifying cultural security risks. In the research of this paper, the cultural security risk problems can be effectively identified, and the cultural security problems in the international exchange of university education can be improved with practical guarantee.

To improve the effect of foreign language teaching in colleges and universities with high efficiency, this paper constructs a BP neural network optimized by multi-data chain by integrating multi-data chain and BP neural network, which effectively solves the shortcomings of BP neural network that is easy to fall into the local optimum. It optimizes the initial value and the threshold value of this neural network. On this basis, the multi-data chain optimized neural network is used to analyze the existing data related to English teaching in colleges and universities, to present the proportion of factors affecting foreign language teaching in colleges and universities in the form of data, and to summarize the reasons for acting the teaching effect and the problems existing in the current foreign language teaching classroom. Teachers and students make positive improvements to foreign language classroom teaching based on the data presented by the neural network to improve the effect of foreign language teaching in colleges and universities. Combined with the BP neural network optimized with multiple data chains, this paper conducted an experimental analysis of students’ concentration, satisfaction with the classroom, and performance. As can be seen from the results of data calculation, after the factors affecting the teaching effect were summarized and corrected, the concentration level of the two students in the experiment was significantly improved, and both remained above 50%. In addition, there was a massive difference in the grades of the same group of students before and after the improvement. The average grade of the control group was 76.47 and the middle grade of the experimental group was 87.6.

In the 21st century, computer technology covers human daily life, including the field of music. This paper uses a quaternion algorithm to describe the gesture posture during piano playing. Combined with the iteratively updated and extended IU-EKF algorithm, it realizes the fusion of piano playing gestures to fix the posture. The recognized piano playing gestures are output to the chord fingering automatic annotation board, the piano audio signal data are preprocessed and input to the 3D space, the annotation area is allocated, and the chord fingering features are extracted using the Boltzmann machine. Through spectral analysis and empirical investigation, we analyze the sound quality effect of the two pianos and the audience’s listening experience. The results show that in the spectral analysis of the two pianos, the time-domain waveforms of the pianos played using the gestures proposed in this paper have durations ranging from 0.75s to 1.25s, and the waveform graphs present triangular shapes, which are better in terms of sound quality. The listeners’ melodic memory of the songs played by two pianos is the best for the two pianos of Chinese art and folk songs, Chinese art and Chinese pop, with the average difference of 5.7899 and 5.6345 respectively. The two pianos form of playing can satisfy the listener’s need of listening to the piano songs to a certain extent.

To realize the safe operation of electric power site, this paper proposes an intelligent recognition technology to automatically identify violations. This study successfully constructs a face detection model for power operation sites by combining deep convolutional neural networks and target detection algorithms. A three-way connected feature pyramid structure containing a neuron self-processing module is adopted, and an accuracy test is completed using a Tri-FPN-based target detection network, significantly improving recognition accuracy. In this paper, we also utilized the on-site images collected by video surveillance equipment, combined with CNN algorithm and HOG feature extraction technology to effectively identify the violations and provide early warning of the breaches of the personnel at the power operation site. MAP curves evaluated the detection performance, and the results showed that the head recognition rate was up to 0.9913, and the accuracy rate of all violations exceeded 0.9350.The high accuracy of CNN-based feature fusion extraction algorithm in the recognition of violations of personnel at the site of electric power operation provides effective technical support to ensure personnel safety.

In this paper, we use space-sensitive crawler technology to obtain the initial data of college students’ employment quality research from the websites or databases of private colleges and universities. We determine the evaluation indexes of college students’ employment quality through data cleaning and conversion of the initial data. On this basis, because of the limitations of the clustering algorithm, grey correlation analysis is used to optimize the clustering algorithm, and the evaluation model of college students’ employment quality based on grey correlation optimization clustering is constructed. Then the clustering analysis of college students’ employment evaluation indexes is carried out. The results show that the main influencing factors of job satisfaction of female graduates in some disciplines such as literature, history and philosophy and education and law are family economic situation Q7 (-1.9328), participation in innovative and entrepreneurial activities Q8 (29.2178), political outlook Q11 (3.0279), and graduation destination Q13 (2.5824), and that this paper’s method is effective in revealing the key factors. This study benefits the enrichment and innovation of the research content of employment quality. It provides theoretical support for solving the problem of future employment quality of college students in private colleges and universities.

With the in-depth development of data intelligence, the design of public environmental facilities has gone beyond mere aesthetics and practicality, and is moving towards a deeper integration with interaction design. This paper addresses this trend, discusses the strategies and principles of interaction design for public environmental facilities, and proposes an innovative single-facility siting problem model (MP) and multi-facility siting problem model (MWP). The article solves and analyzes the optimal siting problems in these models using the advanced new projection contraction algorithm (PC). To practically test the effectiveness of these designs, two sets of empirical experiments are conducted to explore them in depth. The results show that the optimized interaction design of the public facilities substantially increased the frequency of users, especially in the period from 15:00 to 15:10 on rest days, the number of users was as high as 43, of which the highest number of users was 46, mainly among the middle-aged and elderly people, especially the middle-aged group. In addition, this interaction design is excellent in satisfying people’s necessary, spontaneous, and social activities, with satisfaction levels of 0.78, 0.71, and 0.62, respectively. The interaction design strategy proposed in this paper effectively enhances the efficiency of the use of the public facilities and profoundly satisfies the users’ daily needs.

With the development of society, the artistic cultivation requirements for talents are increasing. This paper discusses in depth the main problems in the current reform of art teaching and puts forward targeted solution strategies, aiming to improve the overall quality of art teaching. In the study, the key factors affecting the quality of art teaching in colleges and universities were analyzed using the VAR model, and a multimodal art education knowledge map was constructed using the Viterbi method. Through the ALKPT knowledge tracking model, this paper assessed and scored students’ mastery of art knowledge and incorporated the results into the art classroom quality evaluation module. Based on the scoring data, the art classroom quality features were standardized, and the comprehensive evaluation results were derived through feature fusion dynamic weight assignment. Impulse response analysis showed that the number of returnees who studied abroad to learn about art positively impacted art teaching. The empirical results showed that after the reform of art teaching, the students’ art theory scores increased by 5 points on average, the scores were distributed between 10 and 20 points, and the achievement gap between students was reduced. This study provides a robust strategy and evaluation tool for optimizing art teaching, improving teaching quality and promoting students’ artistic literacy.

This study accurately assessed the overall ecological landscape condition of the city through the environmental landscape pattern index, and specifically explored the NP, LSI, and PD indices of construction land, cultivated land, forest land, grassland, water, and other land. Adopting advanced artificial intelligence and virtual reality technologies, this paper successfully constructs a 3D visualization scene of the urban landscape, which provides a vivid and intuitive perspective for urban planning. Further, based on the planning and design framework of urban landscape ecosystem, this paper clarifies the ecological evaluation elements and landscape design indexes, and uses VR technology to optimize the design of environmental aspects of urban streets. By establishing an optimization strategy database, this paper conducts an in-depth correlation analysis between the optimization scheme’s design indicators and evaluation factors. Comparing the comprehensive ecosystem service area before and after optimization, the results show that applying VR technology in ecological landscape design is highly effective. The integrated ecosystem service area before and after optimization increased from 7611.72m² to 8039.51m², of which the integrated ecosystem service area of “general” grade increased by 324.03m², showing the best effect of ecological optimization. The research in this paper provides scientific basis and technical support for ecological landscape design and new ideas and methods for future urban environmental planning and construction.

This paper proposes a new evaluation model for the current situation and challenges of further media communication of enterprise brands through diversified publicity. A comprehensive evaluation model based on AHP-EWM-TOPSIS is constructed by establishing an evaluation index system for the effect of new media operation and publicity, and preprocessing the indexes with the polar deviation method, combining the hierarchical analysis method and entropy value method for weight assignment. Through practical application to the evaluation of M Company’s publicity strategy, it is found that most employees evaluate the current strategy as average or poor, and the investment in new media operation cost is low. In addition, gender has no significant effect on the use of new media platforms, but the user web video usage rate has reached 90%. Therefore, it is recommended that enterprises adopt scientific and practical theoretical models such as the funnel propaganda model proposed in the paper to improve the efficiency of new media operation and brand communication effect, and promote the development of enterprises.

This paper focuses on the high-quality detection of hidden safety hazards in transmission and OPGW lines, and adopts neural network technology as the research basis. A Faster-R-CNN network structure model is constructed to realize end-to-end target detection by combining RPN and Fast-R-CNN network structure. To further improve the detection accuracy, the BAM algorithm is introduced to enhance the Faster-R-CNN, to realize the accurate detection of hidden dangers in transmission and OPGW lines. This paper also compares the performance of the traditional and improved algorithms, and explores the practical application effect of the constructed model in depth. The experimental results show that the enhanced Faster-R-CNN algorithm significantly improves the correctness of observation in the sky and land regions, with an average accuracy mean value of about 26%, especially when observing field villages, factories, playgrounds, urban areas and swimming pools. Therefore, the improved algorithm proposed in this study effectively enhances the detection capability and accuracy of hidden safety hazards in transmission and OPGW lines.

To cope with the problems existing in the education and Management of students in traditional colleges and universities, improve the management efficiency and realize the informatization of education management, this study proposes a set of optimization paths for the education and Management of students in colleges and universities. In this paper, an intelligent and diversified education management platform is constructed, aiming to strengthen the operation efficiency of the education management team in colleges and universities. By introducing a fuzzy evaluation algorithm to judge the educational management paths of college students and normalizing the input data, empirical evaluation results are derived. Comparative Analysis of the data in the study showed that the optimized educational management path significantly improved students’ daily behavior and mental health status. Students’ average daily online time was effectively controlled between 5.6 and 10.5 hours, the online period was more in line with the healthy work and rest, and the rates of disciplinary behaviors and Internet fraud victimization decreased respectively. At the same time, students’ mental health and learning effects were also improved. In summary, the education and management path for college students proposed in this paper significantly strengthens the management system and comprehensively cultivates students. It provides an effective informatization strategy for education management in colleges and universities.