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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.

Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.

At present, most graphic design methods of 3D animation scenes only get a small part of 3D animation scenes, and all of them are in a 3D coordinate system, with observers as the core, so it is difficult to express the depth information of 3D animation scenes. This project intends to study a plane design method for 3D animation scenes based on deep learning. CNN (Convolutional Neural Network) is used to build a multi-view 3D animation scene generation network, and 3D geometry and structure of objects are reconstructed through multiple or a group of images. On this basis, the feature extraction method of 3D animation scenes is studied, and the collaborative learning model of multiple networks is established to improve the modeling accuracy of 3D animation scenes. The experimental results show that the network model is superior to the method based on multi-view and 0-1 voxel in detecting retrieval performance, and the accuracy rate can reach 91.725%. The multi-view 3D animation scene generation method in this paper has achieved better results than the current advanced methods, which proves that the multi-view feature fusion network proposed in this paper is a more reasonable method to fuse multi-view image features.

Campus safety is related to the healthy growth of students, and the research focus of this paper is to explore the impact of Internet of Things (IoT) technology on campus safety prevention and control. Based on the correlation between ISM and DEMATEL methods, this paper constitutes an integrated DEMATEL-ISM model by using neighbor matrix and reachable matrix to transform each other. It constructs a risk factor model for campus security prevention and control evaluation based on Internet of Things technology according to the modeling steps after soliciting experts’ suggestions through questionnaires. Combined with the SEM model to estimate students’ campus risk identification ability, the path index was used to construct the college security awareness score index to explore the relationship between latent variables (influencing factors) and observed variables (risk identification ability). The results show that every 1% increase in the safety climate of the social environment will lead to the rise of 0.58%, 0.20%, and 0.52% in the on-campus environment factor, the campus safety management team building factor, and the students’ literacy factor, respectively. A 0.17% increase in the on-campus environment will occur with every 1% increase in students’ literacy in higher education. This study contributes to a deeper understanding of the role of IoT technology in enhancing campus security prevention and control. It can provide deep research references for subsequent risk grading evaluation, risk warning prevention and control, and supervision in campus risk management.

For colleges and universities, it is imperative to actively respond to the current new teaching mode and to put information technology into English teaching in the new situation. This paper improves the random forest algorithm by using the sparrow search algorithm to calculate the Gini index score of each evaluation index feature. By addressing the impurities of nodes in the tree, the importance of each index in the forest can be ranked. Further, the improved random forest algorithm is combined with convolutional neural network to construct the obtained English teaching evaluation method to explore the practical effect of the informationized English teaching integration path established in this paper in teaching. The results show that the average scores of the five colleges and universities’ average scores of “technology-enabled teaching” and “awareness and attitude” indicators are high, respectively 4.27 and 4.16. In contrast, the scores of “social support” indicators are low. The average score for “social support” is low, only 3.24. After the teaching practice, the number of “excellent” scores in teaching expansion increased by 15. This paper’s integration path for informationized English teaching has precise results, a certain degree of universality, and serves as a reference for future exploration of the path.

Mathematical modeling as a means of application of mathematical knowledge is used to solve all kinds of problems in the real world, based on the simplification of real scenarios to get the simulation linkage model, and further generate mathematical models. In this paper, we design a teaching method for integrating mathematical modeling ideas into higher mathematics, and construct a path for cultivating modeling abilities. To develop the relevant analysis, a principal component analysis framework for assessing college students’ self-efficacy in modeling ability is proposed, and its influencing factors are analyzed by multiple linear regression. To have a more comprehensive understanding of the current status of students’ modeling ability, students’ self-efficacy for mathematical modeling and mathematical modeling ability were mapped based on the scale and the pre-test paper, respectively. It was found that most of the students’ perceived self-modeling hypothesis ability scored between [4,5] points, and the scores would significantly affect the two dimensions of self-efficacy in model building (0.001) and model analyzing (0.004) ability. Regarding the number of high scores, the experimental class was 13 and the control class was 8. Regarding the percentage of high scores, the experimental class is higher than the control class in all cases. Mathematical modeling ideas integrated into the real-problem teaching mode can improve students’ academic performance compared to traditional teaching modes.

With the development of information technology, music teaching methods are getting more affluent and more prosperous. This paper proposes a model for emotion classification and assessment that integrates traditional music culture elements with information technology in music teaching. The research first combines TextCNN and BiLSTM algorithms to establish the emotion classification model of conventional music. Then it combines PYIN and DTW algorithms to establish the evaluation model of traditional music, which completes the auxiliary efficacy of music informationized teaching. In the emotion classification test of the model, the classification accuracy and F1 value of emotions of different music samples are 82.98% and 75.22%, respectively. The model’s recognition accuracy of the four voices is 86.76%, and the overall effective scoring percentage is 81.98% under different playing abnormalities. This study has had an impact on traditional music evaluation. The model in this paper can be used to classify and evaluate emotions in conventional music, providing more intelligent and high-quality technical services for integrating traditional music into music teaching.

With the continuous development of the computer field, mixed reality technology is increasingly used in various fields of life. This paper aims to create a mixed reality interactive experimental teaching system for economic colleges and develop the overall framework of the system using the target detection algorithm. It first designs the interaction system module based on monocular visual spatial localization. Then it builds a teaching evaluation system based on face detection, recognition, and gesture estimation for the experimental teaching of economic colleges and universities for the mixed reality interaction system. This paper empirically analyzes the proposed mixed reality interaction system for experimental teaching in economic colleges. It can achieve a processing speed of 19 frames per second and a high recognition accuracy of 88.23% in the teaching test. The average scores of each index after the testers’ use are above 4, and the average value of the system’s overall score is 4.66. This study has excellent performance and provides a practical roadmap and reference value for developing mixed reality technology in the direction of experimental teaching.

The rise of mobile Internet has not only changed the business model of many enterprises, but also had a profound impact on the financial management model of enterprises. Based on this, this paper applies blockchain technology to enterprise financial management, using decision trees and information entropy alignment to optimize and promote its digital transformation. Regarding optimizing financial management processes, blockchain technology converts the enterprise’s financial management data into a distributed ledger. It integrates it with information entropy to create a digital financial data decision tree. This paper takes enterprise A as the research object, after optimizing the financial management process with blockchain technology, enterprise A’s financing ability is enhanced, and the current ratio as a whole maintains an upward state, reaching a maximum value of 0.86 in 2021, and then it has been maintained at about 0.8, indicating that the optimized short-term solvency capacity has been significantly improved. The total debt amount of enterprises is decreasing yearly, falling to 35.26 billion yuan in 2023. The amount of owners’ equity has shown an upward trend and has remained at a high level of 25 billion yuan since 2021. Therefore, blockchain technology strengthens the financial management capabilities of enterprises and explores a feasible path to reduce investment risks.