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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.