bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computer Vision Technology Integration and Art Education Practices for Modern Printmaking
Authors: Yan Weina , Wang Dewei , Meng Jun 
Published: December 2024

Abstract
Printmaking has a long history and high artistic value, and it is challenging research to integrate computer vision technology into artists’ printmaking process. In this paper, the line, texture, and frequency features of modern prints are extracted according to their artistic characteristics and then restructured by a convolutional neural network. The parameters of the conversion network are updated using gradient descent, and CLIP is used as a pre-training model to establish a modern printmaking-style migration algorithm. Computer vision technology is used to construct a modern printmaking digital image simulation and synthesis system. Practice shows that the usability of the system is 67.675, which is close to the level of”good”in the process of creating modern printmaking art by art students. The system’s diverse features of real-time interaction, integration of digital resources, virtual scene construction and autonomous learning further enhance the cultivation of modern printmaking creative ability and have high practical significance in art education value.
Optimised design of digital teaching resources for Civic and Political Education in colleges and universities under the integration of big data technology
Authors: Xie Shufang 
Published: December 2024

Abstract
It is impossible to separate the design and development of teaching resources from the teaching of ideological education in colleges and universities. However, the current digital teaching resources for ideological education exist in large quantities and vary in quality in many colleges and universities. In order to tackle these issues, the study employs big data technology for optimization. Specifically, it proposes an Apriori improvement algorithm, which is based on MapReduce parallelism, and an enhanced hybrid K-Means clustering algorithm, which is based on an intelligent single-particle algorithm. The optimized algorithms are then applied to the optimal design of digital teaching resources for civic and political education in a university. When the order of Civics courses is well switched, students have an 81.2% probability of getting “good” grades. The K-Means clustering algorithm divides 31 types of digital teaching resources for civics education in colleges and universities into 3 categories to enhance the quality of teaching.
A Study of Key Elements of Computer Linguistics Extraction Based on Artificial Intelligence NLP
Authors: Wang Liang , Tan Jinlin , Wang Weiming , Chang Wenjie , Zhang Min , Liu Yan , Wang Wei , Shi Baobao , Zhao Pengpeng 
Published: December 2024

Abstract
Key element extraction is an important research field in computational linguistics. Based on the Hidden Markov Model in natural language processing technology, this paper utilizes the Viterbi decoding algorithm, along with its optimization and improvement algorithms, to construct a key element extraction model. This model then extracts the key legal elements from the original corpus of traffic collision litigation cases. To further validate the performance of this paper’s model, a public newspaper dataset released by a university was selected to deeply explore its effectiveness. This paper’s model significantly improves the accuracy and F1 values of the key elements of legal text extraction, reaching 93.28% and 90.83%, respectively, compared to all other models. The model’s extraction effect on the six key elements in the legal text reaches an ideal state, where the F1 value of the extracted element’s sentence results reaches 100%. In comparison to the HMM model in the public dataset, the model in this paper has improved by 10.93%, 8.78%, and 10.29% in the three indexes, indicating its superior performance in key element extraction.
Research on the Strategy of Curriculum Ideology and Politics in Improving the Comprehensive Quality of College Students Based on Intelligent Education Technology
Authors: Li Cheng , Dong Yinxia 
Published: December 2024

Abstract
This paper integrates intelligent educational technology into the political thinking course, constructing a learning system based on recommendation technology to cater to students’ personalized learning needs and enhance their overall quality. This paper establishes that the core function of the learning system is to recommend personalized educational resources for students. The recommendation algorithm based on collaborative filtering and the recommendation algorithm based on content are combined to form a hybrid recommendation algorithm, which is used to design and implement the learning system with B/S architecture. We create the evaluation system for students’ comprehensive quality by selecting scientific indicators and calculating the gray correlation between their scores and comprehensive quality. The results show that the correlation between ideology and morality and students’ comprehensive quality is 0.75836, which ranks the highest among all indicators. Simultaneously, a comparison of the evaluation scores for each index of students’ comprehensive quality before and after the implementation of the ideology and politics learning system in this paper’s course elucidates the significance of incorporating these elements into the course through intelligent educational technology, thereby enhancing the overall quality of students.
From Single Mode to Integrated Parenting: A Study on the Intelligent Level of Student Management and Parenting in Maritime Vocational Characteristics Colleges and Universities by Internet of Things (IoT) Technology
Authors: Chen Maojia 
Published: December 2024

Abstract
Student management and nurturing is an important part of the education system of colleges and universities. To address the issue of student management and parenting work in nautical vocational colleges and universities, this paper develops a student management system that utilizes IoT technology. The RFID system, front-end operation and back-end management system in the Internet of Things technology constitute the main structure of the student management system of this paper for nautical vocational colleges and universities, and the system carries out tests such as multi-tag reading time to verify the effectiveness of this paper’s student management system. The density peak clustering algorithm, undersampling algorithm and improved Adaboost algorithm are used to construct the student academic abnormality prediction model, while the K-means clustering algorithm is used to cluster and analyse the students’ consumption behaviour. The DP-2W-Adaboost model constructed in this paper effectively improves the overfitting phenomenon caused by noisy data, and compared with the DP-Adaboost algorithm, the SE value is improved by 7.91%, the SP value is improved by 3.40%, in the F1 value is improved by 9.60%, and in the G-mean metric is improved by 5.39%. 1057 students with real academic abnormality were found by the DP-2W-Adaboost model in the prediction of student academic abnormality. K-means clustering algorithm clusters student consumption behaviour into 3 categories by analyzing the mean value of total student consumption data and single consumption data, which provides data support for student management and parenting work in nautical vocational featured colleges and universities.
Research on enterprise economic efficiency assessment and management decision-making based on big data analysis
Authors: Du Qian , Sun Libing 
Published: December 2024

Abstract
Enterprise economic efficiency is an important indicator for measuring the input and output of enterprises, and it has a certain guiding role in the formulation of enterprise management decisions. This paper constructs an enterprise economic efficiency assessment model using big data analysis and explores the application of big data analysis in enterprise management decision-making. First, we extract the principal component factors for measuring enterprise economic efficiency indexes using principal component analysis, and then construct the comprehensive evaluation function of enterprise economic efficiency based on this. The particle swarm algorithm is used to optimize the BP neural network, and the PSO-BP neural network model is constructed to intelligently evaluate the economic efficiency of the enterprise. This paper extracts three principal components of enterprise economic efficiency, namely “enterprise solvency and profitability,” “enterprise operating ability,” and “net asset growth ability,” and achieves a cumulative variance explanation rate of 84.431%. The PSO-BP neural network model and the standard BP neural network model satisfy the error criterion of MAPE<0.001 when they are trained about 170 times and 820 times, respectively, and the number of operations of the former is only about 20.73% of that of the latter, which greatly shortens the running time of the model and indicates that the PSO-BP-based enterprise economic efficiency assessment model in this paper is practical. Practicality. This paper serves as a reference for the application of big data analysis in enterprise economic benefit assessment and management decision formulation.
An Exploration of the Application of Virtual Reality Technology in Enhancing the Interactive Experience in Online Education
Authors: Gao Xiaoli 
Published: December 2024

Abstract
Virtual reality is the process of new development in the new era. The continuous development of education technology is always fueled by the application of new technologies in the field of education and teaching, even though the technology has not yet matured. In this paper, 3D scanning technology is used to construct a three-dimensional scene after collecting point cloud data from the online education environment. The motion capture instrument is used to collect the action characteristics of teachers and students in the online education classroom, and the Kalman filter algorithm is used to process the data for the recognition of interactive actions. Based on this, the online education teaching system based on virtual reality technology is constructed to achieve good interaction between teachers and students in teaching. The results of the teaching experiment show that the post-test learning performance of students in the VR group who learned through virtual reality is better than that of the traditional group, and the pre and post-test scores of students’ learning motivation in the VR group are improved in all dimensions, and all of them reach the level of significance (P<0.001). Additionally, the language of students in VR classroom teaching saw a significant improvement (34.99%), and they gained a positive interactive experience. This paper found that virtual reality technology has a positive impact on student motivation and learning outcomes, which lays the foundation for promoting students’ interactive experiences and learning outcomes in online education.
Construction and Application of Personalised Classroom Teaching Model of College English Combined with Recommendation Algorithm
Authors: Liu Qinli 
Published: December 2024

Abstract
This paper primarily designs and implements a collaborative filtering recommendation algorithm, which is based on user, content, and student profiles. It constructs the user’s learning style or learning interest by analyzing the user’s historical behaviors, and then generates recommendation results by searching for the required information in the information resource database. Through the learner’s basic personal information and behavioral data, the learner is profiled, and the user profile is constructed to achieve personalized teaching in the English classroom, thus realizing the personalized classroom teaching mode combined with the recommendation algorithm. The results indicate that the average heat value of text-based English teaching resources in the cloud classroom is 0.220, higher than that of video-based resources. In terms of time commitment, most students have low levels of commitment to online learning, with all of their online learning hours being under 150 hours. The personalized classroom teaching mode can significantly improve students’ overall performance compared to ordinary teaching methods (p<0.01).
Research on the New Path of Business English Talent Cultivation in Higher Vocational Colleges in the New Era
Authors: Ran Li 
Published: December 2024

Abstract
With the advent of the Internet + era, business English presents many new development situations and characteristics, which puts forward new and higher requirements and challenges for the cultivation of higher vocational business English composite talents. Based on the stakeholder model, this paper extracts the participating subjects of the industry-teaching integration project, and their main motivational elements. Based on the synergy model, it identifies the internal and external synergistic elements of industry-teaching integration and builds a model of internal and external synergistic mechanisms, which lays a theoretical foundation for the subsequent proposal of innovative paths of business English talent cultivation in higher vocational colleges. From 64 policies on industry-teaching integration in higher vocational colleges, synergistic elements of industry-teaching integration talent cultivation are identified, and the fuzzy set qualitative comparative analysis method is used to explore the combination of synergistic elements that can improve the effectiveness of industry-teaching integration talent cultivation projects, i.e., the new path of business English talent cultivation. The study found that enterprise size, discipline type, project motivation, project action, and project constraints are five conditional variables of the industry-teaching integration project. The necessity analysis of individual condition variables found that the consistency and coverage of the five variables did not exceed 0.8 at the same time, indicating that none of the five condition variables could be a necessary condition. Under the condition that the 5 conditional variables are combined, 5 innovative paths of collaborative industry-education cultivation of business English talents are obtained, and the coverage and consistency values of the paths are 0.808042 and 0.913024, both exceeding 0.8, which are able to explain adequately the high performance of collaborative talent cultivation in higher vocational colleges and universities.
A Study on the Impact of Digital Marketing Strategies on Consumers’ Purchase Intention - A Case Study of Social Media Advertising
Authors: Liu Guangqian , Zhu Yiming 
Published: December 2024

Abstract
The advent of the social media era has catapulted the advertising industry into a new phase, marked by a decline in traditional media advertising and a gradual dominance of digital media marketing modes that rely on social media platforms. In fact, these modes have become the primary source of revenue for some social media platforms. This paper explores the influence of different qualities of digital media marketing on consumers’ purchase intentions. Using the technology acceptance model, the theory of planned behavior, the expectation confirmation theory, and other consumer psychological analysis tools, the latent variables of perceived usefulness, perceived ease of use, and subjective norms of social media advertisements are established to complete the conceptual model of the mechanism of action as well as the establishment of the corresponding research hypotheses. Questionnaires are designed and randomly selected by consumer users on social media platforms as research samples. The collected data from the processed questionnaires were inputted into the structural equation model for validation. The model’s output confirms the support for all hypotheses, with a significance level below 0.05. Among them, perceived ease of use has the greatest degree of influence on satisfaction, and consumer satisfaction with social media advertisements largely depends on how easy or difficult it is for users to use the advertisements. Perceived usefulness and satisfaction have significant positive effects on consumers’ final purchase intentions, and satisfaction is more direct and obvious. This study successfully reveals the complex path of action between various types of consumer perceptions and purchase intention in social media advertisements, providing direct evidence for the optimization of digital marketing strategies.
A study on the perception of sense of meaning of life and positive psychological communication law based on social media data analysis
Authors: Zhang Hui , Zhu Ming 
Published: December 2024

Abstract
In modern society, social media has become an important platform for individuals to express themselves and communicate their thoughts and emotions, which profoundly affects our perception of the sense of meaning in life and the spread of positive psychology. The research design for theme identification and sentiment analysis based on social media data includes five steps: data collection, data cleaning, TF-IDF calculation, theme clustering, and sentiment analysis. In this paper, we take the “Xin Guan epidemic” as a case study to identify the core topics of discussion on the sense of meaning of life and assess the sentiment tendency of users under these topics so as to reveal the influence of the perception of the sense of meaning of life on the dissemination of positive psychology on social media. A total of 11 topics related to the sense of meaning of life were obtained from the microblogging platform, and each topic effectively promoted the positive psychology of social media users. It is shown that the perception of the meaning of life is positively related to the spread of positive psychology.
Research on the role of computer-aided design technology in the innovation of ceramic ware surface decoration
Authors: Zhang Xingrong 
Published: December 2024

Abstract
With the development of the times, on the basis of inheriting traditional ceramic decoration, modern ceramic decoration is also changing, and it is moving towards personalisation and diversification. In this paper, firstly, we preprocess the images of the decoration features on the surface of ceramic vessels and use the LBP feature extraction method to extract the detailed features on the surface of ceramic vessels, transform them into HOG data, and represent the pattern information by histogram. Subsequently, an attention mechanism is added to achieve accurate segmentation of ornaments on the surface of ceramic vessels by learning the semantic features of the image. Then, based on the CGM model, the extracted ornament is deconstructed and restructured to design new ornament elements, which are applied to the surface of ceramic vessels. The investigation of the market demand for ceramic ware surface decoration was done to create a data base for ceramic decoration design. The research results show that 53.456% of users buy ceramic ware mainly because of daily use, and 49.563% pay more attention to the color pattern of ceramics. In the characteristic colours of ceramic patterns completed in the design, the characteristic colours 1→2 and 1→5, the correlation between the two groups of characteristic colours is strong, and the intensity is 3.554 and 4.265, respectively.
Strategies and Practices of Artificial Intelligence Technology in Open Education Serving Elderly Education
Authors: Qi Zhiming 
Published: December 2024

Abstract
This paper discusses the study of open education services for the elderly education and proposes the Internet + elderly education model. Two senior classes were selected as the research object and divided into experimental and control groups, with the experimental group adopting this paper’s education model and the control group adopting the traditional education model. After the questionnaire reliability test, the online and offline distribution of questionnaires to obtain the data of this paper, borrowing the independent samples t-test in the knowledge of statistical theory, from the theoretical knowledge, basic skills, and emotional value dimensions of this paper’s educational model for verification and analysis. The data show that the reliability and validity of the questionnaire are 0.870 and 0.8797, respectively, which meets the rigour of the questionnaire. In addition, there is a significant difference (P<0.05) in theoretical knowledge, basic skills, and emotional value of this paper’s educational model, which indicates that this paper’s educational model has an important effect of enhancing the practice of open education to serve the elderly education, and puts forward a three-point strategy of artificial intelligence technology in open education to serve the elderly education.
Study on Improving the Communication Effect of Ideological and Political Education in the Face of New Media Environment
Authors: Su Yanhua , Xu Dan , Hu Xuefeng , Zhang Lu , Zheng Dan 
Published: December 2024

Abstract
According to the problems of ideological and political education communication in the context of new media, the factors influencing the effect of ideological and political education communication in the new media perspective are elicited. Combined with relevant resources and experts’ opinions, the questionnaire design is completed directly, and research data is obtained by distributing questionnaires. A comprehensive Pearson correlation coefficient and a multivariate linear regression model are used to explore the communication effect of ideological and political education in the face of the new media environment. The data revealed that the regression equation for the dissemination effect of ideological and political education is as follows: 0.66+0.357* Communication subject (X1)+0.243* Communication medium (X2)+0.199* Receiver (X3)+0.186* Communication environment (X4). This equation demonstrates significance at the 0.05 level and proposes a two-point strategy to enhance the dissemination of ideological and political education in the context of new media.
Strategies and Practices of Intelligent Algorithms in Dynamic Allocation of Teaching Resources in Primary Education
Authors: Sun Yanting 
Published: December 2024

Abstract
Based on an analysis of the current state of teaching resources in primary education, this paper summarizes the challenges these resources are currently facing. In order to improve the above situation, it is proposed to introduce the theory of resource dynamic allocation strategy into primary education teaching. The fuzzy association algorithm, which leverages big data technology, extracts resource features, calculates the fuzzy association feature quantity of teaching resources, and facilitates the integration of these features in primary education through priority scheduling and dynamic allocation techniques. Based on the actual situation, determining the constraints and objective function, and finally completing the mathematical modeling work, the genetic algorithm can be used to solve the optimal dynamic allocation strategy of educational teaching resources. This paper used selected experimental data from simulation experiments to verify the dynamic allocation model of teaching resources. The recall of this paper’s method (0.261) is significantly higher than that of the other two methods (0.163, 0.155), and similarly, this paper’s method has superiority in utilization, load balancing, latency, efficiency, throughput, and practical satisfaction. This study is able to achieve efficient teaching resource allocation and provide better teaching resources for primary education teaching practice.