bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Innovative Integration of College Students’ Mental Health Education and Civic Education Paths in the Age of Artificial Intelligence
Authors: Dai Chenglong , Wang Jing 
Published: February 2024

Abstract
This article takes personalized education as the starting point, discusses the application in mental health education, and uses the plain Bayesian classification model to classify college students’ mental health in a refined way. The article proposes an evidential reasoning timeliness evaluation model for calculating the total assessment value of ideological and political education in colleges and universities and its actual effect to evaluate the ideological and political education quality. In addition, combining mental health education and ideological and political education, this study constructs an innovative collaborative parenting education model. The empirical analysis shows that the overall level of students’ mental health and Civic and Political learning quality in college L exceeds 2.5 points. By building a model of coupling and coordination degree of mental health and civic education, the study found that the integration benefit index of the two showed an upward trend in 10 years, growing to 0.97852. The coupling and coordination degrees in 2022 reached 0.996332 and 0.911752, respectively, which showed a reasonable level. Through the educational model proposed in this paper, the integration of mental health education and ideological education is more effective, which provides an effective educational method for colleges and universities to improve the overall quality of students.
Research on the Construction and Application Effect of Autonomous Learning Model in the Teaching of Tourism English Majors in Colleges and Universities
Authors: Cui Gaili 
Published: February 2024

Abstract
Autonomous learning is a hot issue in current research in learning. The article firstly constructs an autonomous learning model based on dynamic fuzzy logic (DFL) theory and describes the independent behavior rules in detail. Further, a learning algorithm for the independent learning subspace is proposed. To verify the effectiveness of the DFL-based autonomic learning model, the authors selected college students majoring in English for Tourism as experimental subjects, divided the students into experimental and control groups, and compared their test results before and after the autonomic learning teaching experiment. The study results show that in the six-month experiment, the experimental group significantly improved in three aspects: study time, independent learning ability, and learning achievement. Specifically, upper-middle and middle-level students excelled in independent learning ability. In contrast, students at the lower-middle level showed significant improvement in terms of study time and academic performance, and these differences reached the level of extreme significance (p<0.01). In addition, in terms of the level of learning autonomy, the experimental group has higher indicators of learning autonomy level than the control group in all six dimensions, especially in the dimension of learning environment, the difference between the experimental group and the control group is as high as 52.53%. The study proves the effectiveness of the autonomy learning model based on dynamic fuzzy logic in enhancing students’ autonomy learning ability, especially in promoting the learning development of students at different levels showing significant advantages.
The promotion of common wealth through tourism industry development: the mediating role of tourism professionals
Authors: Wang Xiaomin 
Published: February 2024

Abstract
In the context of shared wealth, the tourism industry, as an essential economic driver, the quality improvement of its human resources is crucial for promoting the development of the industry and realizing the balance of social wealth. In this study, a set of evaluation systems for tourism professionals is constructed based on the competency iceberg model, and the index weights are calculated using the hierarchical single sorting method. Further, the B.P. neural network evaluation method was improved by particle swarm optimization algorithm, and PSO-SVM neural network model was created to evaluate tourism professionals. Taking City A as a case study, the development of tourism industry and its deficiencies were deeply analyzed. The results show that about 36.40% of the residents believe that developing the tourism industry helps improve the living standard and promote shared prosperity. In comparison, 48.36% of the residents reflect the lack of local tourism professionals. In the comprehensive evaluation of tourism talents by individuals, residents and tourists, the mean scores of the indicators of responsibility, work motivation and complete psychological ability were high, respectively 4.49, 4.53 and 4.70.The study not only highlights the crucial role of tourism professionals in promoting the development of tourism industry, but also provides empirical evidence and solid suggestions for future talent cultivation and development strategies in tourism industry.
Exploring the teaching mode of college students’ psychological education under artificial intelligence technology guided by cognitive-behavioral theory
Authors: Wang Zhixian 
Published: February 2024

Abstract
Under the current background of increasingly fierce social competition, the psychological pressure of college student groups is increasing, and mental health problems are becoming more and more significant. Based on the cognitive model of mental health and the theory of cognitive-behavioral psychotherapy, this paper constructs a comprehensive mental health education mechanism covering school, family and society. A mental health abnormality detection device was developed in the study, which integrates a mental health detection model and an early warning mechanism, aiming to detect students’ mental health problems in time. Empirical Analysis shows that the recognition accuracy of the mental health abnormality detection model constructed in this study is as high as 0.8748. Regarding the assessment of teaching effectiveness, the pre- and posttest changes in the mental health scores of the control group and the experimental group are significant, with the test value of the difference between the control group being 1.242, while that of the experimental group is 6.480. The average posttest score of the experimental group is 6.15, which is a decrease of 11.75 compared to that of the control group. This result indicates that the mental health education model proposed in this paper can significantly enhance and effectively improve students’ mental health. This study provides an effective teaching method for college students’ mental health education, which can help guide the practice of mental health education in schools.
Art and Design Education and Students’ Creative Thinking Cultivation in Colleges and Universities in the Internet Era
Authors: Zou Dongfang 
Published: February 2024

Abstract
This paper analyzes the impact of MOOC-based flipped classroom model on students’ creative thinking in art and design education in colleges and universities. Computer-aided design courses and flipped classroom methods focus on the relationship between flipped classroom and students’ creative thinking. Using Bayesian quantile regression model for statistical analysis, the experiment was carried out in two parallel classes in a university in H city, using flipped classroom and traditional teaching mode respectively. The results showed that the experimental class improved significantly more than the control class in creative thinking skills, especially in projecting the future and evaluating power. The conclusion shows that the flipped classroom model effectively improves students’ creative thinking ability, especially in association and compatibility.
Research on the Coupling of College Academic Style Construction and Great Ideological Politics under the Guidance of the Concept of Three-Whole Parenting
Authors: Li Shanjiang , Tang Laihong 
Published: February 2024

Abstract
The construction of academic style is the central link in implementing the fundamental task of cultivating moral integrity in colleges and universities, and it is also a meaningful way and carrier for realizing the all-embracing, all-encompassing and all-encompassing cultivation of human beings. Based on the four-stage cycle theory of PDCA (Plan-Do-Check-Act), the article constructs a comprehensive evaluation system focusing on the construction of academic style and the evaluation system of the quality of the significant ideological and political cultivation with the goal of value shaping. In the study, the method of variance maximization is adopted to determine the weights of evaluation indexes, and a genetic algorithm is introduced to optimize these weights to solve the optimal linear programming problem of variance maximization. The study establishes a fuzzy comprehensive evaluation and coupled coordination model through this method. Taking University S as an example, the article conducted a comprehensive review of the quality of the academic style construction and the quality of the significant ideological and political education. It used the comprehensive development index to analyze the level of the coupled and coordinated development of the two. The results show that in the construction of academic style, the weight value of students’ learning behavior is 0.2565, while in the evaluation system of the quality of considerable ideological and political education, the weight value of teaching effect is 0.2564. These two fields’ coupling coordination levels increased 1.33 times from 2010 to 2019. This indicates that the higher the level of coupled and coordinated development of college academic style construction and more excellent ideological and political education, the more helpful it is for colleges and universities to realize the goal of cultivating moral character. The study in this paper provides a practical evaluation and optimization framework to promote the comprehensive improvement of the quality of college academic style construction and considerable ideological and political education, and then better achieve the educational goal of establishing morality and educating people.
Discussion on the use of fashion elements in ceramic design in the context of big data
Authors: Ye Jiabei 
Published: February 2024

Abstract
In the context of the significant data era, this study explores the application of fashion elements in ceramic design. By integrating modern aesthetics and traditional craftsmanship, it realizes the innovation of ceramic design and promotes the development of ceramic industry. A neural network-based assessment model, especially BP artificial neural network, is used to quantitatively analyze and apply fashion elements in ceramic design. First, the basic theory of neural network, including MP model and BP algorithm, is introduced. The study established a network model with a 5-30-5 structure by training 800 sets of color samples. The experimental results show that the network reaches the mean square error target (MSE=1e-7) after 1516 iterations, proving the model’s effectiveness in dealing with nonlinear color semantic mapping. The analysis of the prediction accuracy of the color scheme shows that the maximum errors are 2.22, 1.93, and 1.36 in the L, A, and B components, respectively. These results are lower than the national standard color difference range, indicating that the proposed model has practical application in ceramic design. The neural network model can effectively integrate fashion elements into ceramic design, providing a new perspective and technical support for traditional ceramic design.
Theoretical Advancement of Xi Jinping’s Socialist Thought with Chinese Characteristics in the New Era under the Construction of Knowledge Map
Authors: Wang Lili 
Published: February 2024

Abstract
This paper discusses the theory and practice of constructing knowledge graph under the guidance of Xi Jinping’s socialist thought with Chinese characteristics in the new era. The study first adopts the Transformer technique based on the BERT model for effectively extracting entity relationships. Then, the article proposes an entity alignment method based on Neural Tensor Network, which enhances the semantic and structural information utilization of entity embedding vectors through optimized NTN and joint knowledge representation learning. In addition, the study employs a knowledge graph link prediction method that combines semantics and paths to fill in missing relations, and applies probabilistic soft logic to solve the inference problem of non-deterministic knowledge. Ultimately, the effectiveness of the constructed knowledge graph is demonstrated through empirical analysis of Xi Jinping’s theoretical dataset of socialist thought with Chinese characteristics in the new era. The results show that the method is more effective in dealing with polysemous words and participle errors than traditional methods, improving the data structuring level. For example, the accuracy of entity alignment is significantly better than the baseline algorithm on different datasets, e.g., on the DBP-WE dataset, its Hits@1, Hits@10 and MRR values are higher than those of the baseline by 4.01-29.49, 1.99-31.05, and 0.036-0.289, respectively. The method of this paper is remarkably effective in improving the performance of the entity alignment task, which is helpful in understanding and applying the Xi Jinping’s thought of socialism with Chinese characteristics in the new era provides new theoretical perspectives and practical methods.
A study of paraphrase meta-language in linguistic domains in the age of artificial intelligence
Authors: Peng Tongtong 
Published: February 2024

Abstract
This study delves into paraphrase meta-language for linguistic domains in the age of artificial intelligence. The study includes text preprocessing, text representation based on vector space modeling, statistical disambiguation, feature selection, and LDA topic modeling application. The research results show that these methods can effectively extract and understand paraphrased meta-language. The thematic distribution and dynamic changes of paraphrased meta-language are revealed by LDA modeling analysis in 4623552 Twitter data and 532565 linguistic documents. In addition, this study empirically analyzes paraphrase meta-language based on lexical understanding and finds that the average correctness of the annotators meets the expected range in all types of polysemous words. In the era of artificial intelligence, the study of paraphrase meta-language can bring new insights to linguistics, especially showing its value in understanding and processing large-scale linguistic data.
Innovative Reform Path of Curriculum Civics Integrated Teaching Mode under the Background of Technological Empowerment with Multi-cluster Structural Modeling
Authors: Shi Quanyu 
Published: February 2024

Abstract
This study explores the innovative reform path of the comprehensive teaching model of course civics in the context of technological empowerment with multi-cluster structural model. Fang this paper takes S city colleges and universities as an example, based on the theory of planned behavior, it establishes the evaluation index system of influencing factors. Using DEMATEL method to process the scoring data of 15 experts, the influence matrix between the factors was shown, and the fundamental values such as influence degree and influenced degree were calculated. These key factors were then validated using factor analysis and multi-cluster structural modeling. The study results show that the improvement of teaching methods significantly impacts the teaching effect, in which the centrality of “global awareness” is the highest at 10.188. To improve the teaching effect, colleges and universities need to prioritize improving teaching methods, especially the strengthening of the cultivation of global awareness, and combine with the means of scientific and technological empowerment, to carry out the innovation of curriculum Civic and Political Education.
A Multiple Teaching Model for Chinese Culture Integration in English Translation Courses with Multiple Data Chain Networks
Authors: Wang Xin 
Published: February 2024

Abstract
This study investigates how to integrate Chinese culture into English translation courses in a multidata chain network environment to develop a multifaceted teaching model, enhance students’ cross-cultural communication skills and cultural understanding, and improve the effectiveness of English teaching. Neural networks and regression models were used to extract and analyze the artistic elements, and the design and implementation of an English teaching model based on cultural integration were studied. The results show that through the “five lines of integration” teaching mode, the effective combination of culture, knowledge and literacy is realized. The comparative analysis before and after the teaching shows that the mean value of the students’ English scores is increased from 85.204 to 94.568, which indicates that the teaching mode effectively improves the students’ English proficiency. In addition, correlation and regression analyses further confirmed that cultural support, teacher support, course quality, academic performance, and student satisfaction significantly positively affected teaching effectiveness. This study proves the effectiveness of integrating Chinese culture into English language teaching and provides new perspectives and methods to improve the quality of English language teaching.
Research on Intangible Cultural Heritage Outreach Translation Based on Natural Language Processing Technology
Authors: Dong Nan 
Published: February 2024

Abstract
In this paper, a CNN-Bi-LSTM-CRF model is formed by using convolutional neural network combined with Bi-LSTMCRF, and at the same time, with the help of the attention mechanism, which is used to carry out attribute extraction research on the attribute-valued texts in the intangible cultural heritage annotated intangible cultural heritage attribute corpus as well as the validation of the automatic text detection system for the error text, and the results show that the CNN-Bi-LSTM-CRF model performs optimally in the attribute extraction of “heritage type”, with a score of 97.382. The results show that the CNN-Bi-LSTM-CRF model has the best performance, with a score of 97.382 in the attribute extraction of “heritage type”, and the average detection error rate of the traditional system is (17.5%,14.20%) in the comparison of the detection error rate of handwritten and printed text, respectively, the average detection error rate of Bi-LSTM-CRF is (7.19%, 6.15%). The average detection error rate is (7.19%, 6.15%). CNN-Bi-LSTM-CRF samples and categorizes the translations of the three NRLs, and finds that there is a considerable proportion of mistranslations in the linguistic dimension (30.2%), cultural dimension (8.05%) and communicative dimension (10.07%). Finally, it explores tourists’ satisfaction with the destination through outreach factors. Most foreign tourists’ affective polarity scores for the Great Wall range from 0.1 to 0.3, with primarily positive emotions.
An Innovative Demonstration of Functional Linguistic Ideas for Teaching English in Colleges and Universities Based on Multi-objective Optimization Models
Authors: Tian Lili 
Published: February 2024

Abstract
Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional linguistics to innovate the teaching method, focusing on constructing a personalized English teaching resources recommendation system to assist English teaching. Among them, in the teaching resources recommendation module, the collaborative filtering algorithm is improved by calculating the learner trust degree and similarity degree, then describing the learner features and learning resources features respectively, constructing the multi-objective optimization function, and solving the optimal value by using the hybrid differential evolutionary algorithm. The constructed English teaching resources recommendation system is applied to actual teaching. For the answer situation of knowledge difficulty category 5, the highest level students can still maintain the average correct rate of 55.21%. In the comparison experiment of innovation effect demonstration, the experimental group and the control group reached statistical significance in fluency index (P=0.038<0.05) and logic index (P=0.001<0.05). This study can promote the practicability of English teaching and increase the effect of cultivating applied talents.
Exploration of the Influence of Traditional Art Colors on the Visual Communication Effect of Modern Graphic Design Based on VR Models
Authors: Xu Hai 
Published: February 2024

Abstract
This paper discusses how virtual reality (VR) technology effectively integrates traditional art colors in modern graphic design and its impact on the visual communication effect. Through structural equation modeling (SEM) and K-mean clustering algorithm, the application of traditional art colors in modern graphic design and its influence on visual communication are studied and analyzed. The results show that applying standard colors has a significant impact in contemporary graphic design, especially in emotional resonance, cultural symbols, guiding attention and brand identity. The application of conventional colors has a high visual communication effect with modern graphic design, and there is a significant positive correlation between the dimensions. Finally, through K-mean clustering analysis, the optical communication effect of graphic design is divided into four types, in which emotional resonance and cultural symbols occupy an essential position in the visual communication effect. The research results provide a theoretical basis for applying traditional delicate art colors in modern graphic design and a practical reference for designers in the actual design process.
Digital Technology-Driven Music Teaching Model Innovation and Students’ Artistic Literacy Enhancement
Authors: Yao Yao , An Xue 
Published: February 2024

Abstract
This study examines the use of digital technologies in music teaching, focusing on the combination of musical feature extraction, attention mechanisms, and convolutional neural networks, and the potential impact of these technologies on improving students’ artistic literacy. The study explores digital technology-driven innovations in music teaching models and their impact on improving students’ artistic literacy. The study preprocesses musical features and uses Mel’s inverted spectral coefficients to extract key audio features. The feature extraction process is further optimized by introducing the attention mechanism and convolutional neural network to ensure the effectiveness and relevance of the music teaching content. On this basis, a music teaching aid platform is designed, which concentrates on resource sharing, openness and professionalism. Through questionnaire surveys and empirical analysis, this study conducted an in-depth analysis of the platform’s willingness to use, online learning behavior, and system performance. The results show that relative advantage, perceived quality and perceived usefulness are the main factors affecting the willingness to use. Meanwhile, there are significant differences in task participation behavior among students of different genders. The system performance test shows that the platform can effectively support multi-user concurrent operation and meet music teaching needs. To summarize, the study proves the potential of digital technology in enhancing the effectiveness of music teaching and students’ artistic literacy. It provides a valuable reference for the future application of educational technology.