bluuebunny/crossref_metadata_2025 huggingface dataset:
Intelligent Promotion Strategies for the Integration of Traditional Culture and Ideological and Political Education in Higher Education
Authors: Zhao Yu
Published: November 2024

Traditional culture and civic education are two important parts of the higher education system. Based on knowledge graph technology, this paper explores a strategy to promote the intelligent integration of the two. Firstly, we construct a knowledge map for the field of civics and traditional culture and optimize the traditional named entity recognition algorithm by using the BERT module for word feature extraction and pre-training. Through the search and recognition of stored domain knowledge, a smart Q&A system that integrates ideology, politics, and traditional culture can be constructed and used for teaching. The F1 value of the constructed BERT-BiLSTM-CRF algorithm is 76.7, which outperforms the comparison algorithm, and the ablation experiments show that the F1 value of the algorithm decreases by 7.9% when the BERT module is missing, which verifies the reasonableness of the design of this paper. The results of the teaching experiments on the performance of Civics and the interest in learning traditional culture show that the application of an intelligent Q&A system for integrated teaching effectively improves the students’ performance in Civics and the interest in learning traditional culture.
Content dissemination strategy of political ideological education in colleges and universities under the new media environment
Authors: Liu Yanqin , Deng Chao
Published: November 2024

New media technology can provide powerful support for the dissemination of political and ideological education content in colleges and universities. This paper relies on the hybrid recommendation algorithm to construct a personalized recommendation system for ideological and political education content in colleges and universities, which provides a brand new strategy for the dissemination of ideological and political education content. The popular resource penalty factor and time decay penalty factor are introduced into the original Pearson similarity calculation to improve the accuracy of the recommendation algorithm. This paper employs the statistical analysis method to examine the influence of the personalized recommendation system on the spread of ideological and political content in colleges and universities. The big data analysis module in the system predicts users’ forwarding behavior and the system’s dissemination effect, while the SIR model, which incorporates forwarding probability, conducts simulation experiments to replicate users’ ability to disseminate information. It has been found that the vividness, originality, interactivity, and interestingness of the recommended content of the system have a significant impact on the number of user likes and positive comments, which measure the dissemination effect. The big data analysis module is able to predict the forwarding behavior of users and the depth of dissemination of civic and political education content with accuracy exceeding 90% and 80%, respectively. Simultaneously, the system’s core users disseminate civic and political education content quickly and widely, allowing the system to effectively disseminate these contents in this paper.
Functional Analysis of Physical Education Program Construction in Colleges and Universities on Promoting Students’ Mental Health
Authors: Pei Hongliang
Published: November 2024

College and university physical education programs play a crucial role in enhancing students’ fitness and mental health, serving as a vital means of fostering their mental well-being. This paper outlines the structural design of college physical education courses and suggests effective strategies to enhance students’ mental health through their construction. This paper utilizes the mental health level as an index to gauge the impact of college physical education curriculum construction on students’ mental health. It develops a method for evaluating the mental health of college students using a Krill swarm algorithm-optimized support vector machine. It uses the evaluation indexes of somatization and depression from the SCL-90 as inputs for the KH-SVM evaluation model, which then outputs the students’ mental health status. We selected 660 students enrolled in 2022 at W College as research subjects for the mental health assessment. With the exception of the terror factor (P=0.145>0.145), the P values for other mental health level factors like somatization and obsessive-compulsive disorder before and after the optimization and construction of college physical education courses are less than 0.05, indicating significant differences and improvements in the psychological problems associated with these factors. After the optimization and construction of physical education courses, the total mean score of SCL-90 students was 146.73, which was 28.78 points lower than the score of 175.48 before the experiment, which was at a healthy level.
Music genre classification using deep learning: a comparative analysis of CNNs and RNNs
Authors: Xu Wenyi
Published: November 2024

Music categorization as a key component of music information retrieval is an important means of dealing with massive amounts of music information. Based on this, this paper proposes the use of deep learning algorithms to automatically identify music genre factions in a classification method. After denoising the audio using wavelet thresholding, the method of combining audio characteristics and spectral image features is used to enhance the audio data, short-time Fourier transforms and Meier transforms are used to realize audio feature extraction, and then convolutional neural network and recurrent neural network are used as the classifiers respectively to construct the music genre classification model. The GTZAN dataset is used as an experimental object to explore the effects of data enhancement and feature extraction on the music classification effect and to compare and analyze the performance of the CNN model and the RNN model. When the data enhancement methods based on audio features and image features are used simultaneously, the music classification accuracy of the model is above 90%, and the model using a short-time Fourier transform to extract audio features has a better classification effect. The CNN model is 1.3% more effective than the RNN model in classifying music genres, and this result is still consistent even if the audio is processed with variable speed pitch change and structural change, so the CNN model has better music genre classification.
Construction of Virtual Reality Teaching Environment for Civic and Political Education in Colleges and Universities and Its Teaching Quality Assessment
Authors: Gou Wenyan , Zhang Xi
Published: November 2024

Based on the need to adapt to the future development of education, the application of VR technology in the field of education is becoming more and more extensive and in-depth, and the application of VR technology to ideological and political education in colleges and universities is inevitable to adapt to the development of the times. After building a virtual reality online teaching environment for ideology and politics, the study designed a quality assessment system for ideology and politics teaching based on a VR teaching environment based on the principle of indicator construction and combined the subjective hierarchical analysis method and objective CRITIC method to assign the weights of indicators. By combining the fuzzy comprehensive evaluation method with five schools as examples, the research sample’s teaching quality was comprehensively evaluated. The findings indicate that the KMO value for each dimension is greater than 0.7, and the indicators have a strong correlation. Among the evaluation results of the four dimensions, the comprehensive evaluation of the C2 school’s Civics education environment is poor, but the comprehensive evaluation of the Civics education input and Civics education effectiveness is excellent, while the average value of the water of the VR Civics teaching quality of the five schools is at 63.01, which is on the passing line.
Design and Implementation of Virtual Reality Teaching Scenarios for Chinese Language Chinese Education
Authors: Guo Jian
Published: November 2024

By superimposing virtual information on the real teaching scene, it can inject new vitality into the teaching of Chinese in the Chinese language with the characteristics of and immersive learning experience. On the background of exploring virtual reality technology in Chinese education of Chinese language, the article introduces virtual reality technology in teaching, realizes voice interaction and gesture interaction functions, and builds a virtual reality teaching scene by using natural interaction technology. The learning experience test experiment has shown that the virtual reality teaching scene design proposed in this paper has achieved preliminary results with an average score of above 3.6. Through the analysis of student’s learning experiences and the pre-test and post-test analysis of learning motivation in the experimental and control classes, it is learned that the virtual reality teaching scene designed in this paper for Chinese language education has achieved good results.
A Study of FEDER Reconstruction of Ideological Education Model in Digital Media Technology and VR Environment
Authors: Li Qingxiang , Qin Liping
Published: November 2024

Digital media technology plays a crucial role in reproducing historical scenes and facilitating virtual interactions with political figures in ideological education, with virtual reality (VR) serving as a crucial technological support for the reconstruction of a new ideological education model. In order to improve the teaching immersion, this paper designs a multimodal gesture recognition algorithm. The acquired gesture data are regarded as curves, weights are assigned according to the degree of gesture fluctuation, and weight-based Pearson correlation coefficient algorithms (WPCCs) are proposed to calculate gesture similarity. The data and model are trained in a distributed manner using the FEDER learning technique finally. The optimized algorithm achieves a recognition rate of 96.66% for eight common gestures, which is 37.36% less time-consuming than the DTW algorithm. After applying the algorithm to virtual reality ideology education, students experienced a strong sense of presence (mean = 4.12); the post-test learning motivation score was 0.42 points higher than the pre-test (p<0.05); and the mean scores of perceived ease of use and emotional experience evaluations were 3.97 points and 4.33 points, respectively. It indicates that the algorithm in this paper has excellent performance and application efficiency.
Informatization Construction and Innovative Change Path of Educational Management in Higher Education Institutions
Authors: Li Xiaobo , Zhu Wei , Peng Yu
Published: November 2024

This paper adopts a combination of system dynamics and entropy power methods to establish a system model suitable for exploring sustainability and informationization construction of education management in higher vocational colleges and universities. It explores the influencing role of each element in the system and analyzes the causal relationship between each factor. Then, using structural equation modeling, the mediating role of teachers’ educational technology competence was analyzed to explore an innovative path for management information technology construction in higher vocational colleges. The results show that the system sustainability of performance assessment effectiveness, informationization training growth rate and information resource funding growth rate are all greater than 16. The management effectiveness scores for infrastructure funding utilization rate and information management personnel growth rate in 2025 are 24.7317 and 24.3988, respectively. The educational technology competence of teachers in higher vocational colleges and universities plays a mediator role in the situation of informatization infrastructure construction (indirect effect value of 0.112). Therefore, in the construction of informationization of education management in higher vocational colleges and universities, more attention should be paid to the aspects of informationization training, information resources, performance, infrastructure, talents and teachers’ education technology.
A Study of Evolutionary Trends of Classical Music Works Based on Data Mining
Authors: Li Yeye
Published: November 2024

Classical music is one of the most influential types of music, and Chinese classical music has entered a promising period of development after decades of accumulation. In this paper, some works are selected from many different types of classical music datasets to be produced as datasets, and the data of classical music works are pre-processed by labeling and segmentation. The chromaticity vectors of classical music works are solved using PCP features and combined with the MFCC algorithm to obtain the note feature changes of classical music works. The acquired classical music features are input into the bidirectional LSTM model, and then the self-attention mechanism is introduced to assign weights to the classical music feature vectors so as to realize the chord recognition of classical music works. The average accuracy of feature recognition of classical music works using chromaticity vectors is 63.47%, and the average misdetection rate and omission rate for notes of classical music works are only 0.11% and 0.08%, respectively. When the Quarter Length grows from 0.2 to 9.9, there are obvious discrete changes between different classical music works data, and the frequency of some Pitches is only within 10 times. The values of scale variance of classical music works in the duration fluctuated between 0.01 and 0.14, and the maximum accuracy of the chord recognition model of classical music works was 0.8379. Combining data mining and deep learning can understand chord and note changes in classical music works, and provide support for exploring the evolutionary trend of classical music works.
A real-time risk assessment model for cross-border financial transactions based on big data technology
Authors: Bao Mengrui
Published: November 2024

The study applies the method of resampling to deal with unbalanced financial transaction data, which is resampled by the method of majority class weighted minority class oversampling. After data processing, the VaR-GARCH financial transaction risk assessment model is constructed. The financial transaction risk assessment method of this paper is compared with other risk assessment methods to test its assessment performance. Subsequently, taking the carbon financial market as an entry point, the trading price data of seven global carbon financial markets from 2021 to June 28, 2024, are selected for the study to assess the risk of the carbon transnational trading market in real-time. The risk assessment efficacy of this paper’s risk assessment model on both the AP and LC datasets has an overall advantage. Among the seven global carbon markets, the EU has the most drastic fluctuation in transaction prices, while the Chinese carbon market is the smoothest. The transaction price averages from highest to lowest are California-Quebec (85.59), South Korea (72.49), U.S. Regional Greenhouse Gas Emission Reduction Program (47.24), U.K. (44.80), China (37.26), New Zealand (34.35), and EU (34.34). California-Quebec had the highest average transaction price, while the EU had the lowest average transaction price. Transaction prices in China are the most stable, and log yield trends in the UK and South Korea are similar. The top three markets in terms of value-at-risk VaR are California-Quebec, South Korea, and the EU, and the smallest is the UK market.
Optimizing large-scale data processing in the digital economy using high-performance computing techniques
Authors: Dong Fei
Published: November 2024

In order to solve the current issues of not making the most of scheduling strategies, uneven load, and using too much energy in high-performance computing for the digital economy, this paper creates a task scheduling algorithm that is based on both a genetic-ant colony algorithm and an energy-aware task scheduling algorithm. The first algorithm leverages the robust search space solution capability of the genetic algorithm to transform the optimal solution into an initial pheromone, thereby enhancing global searchability and feedback. The latter reduces overall energy consumption through ETF and NCM operations and allocates appropriate computing nodes for each computing task. The CSI 300 index is used as the research data to carry out empirical analysis of large-scale data processing optimization. Under the scheduling strategy optimized by the algorithm in this paper, the performance of the task scheduling strategy is improved by 2.6 times compared with that of the unoptimized one, and the difference between the capacity share of the memory storage layer and the SSD storage layer is no more than 6%, so as to achieve the load balance between the storage layers. The average CPU utilization remains low at 18.12%, with the running total job power consumption primarily concentrated in the 10w-38w range, which accounts for about 56% of the overall power consumption.
Optimization path of blockchain technology in enterprise economic and financial management
Authors: Hu Haibo , Nordin Nurhaiza Binti , liujinbo
Published: November 2024

In the face of the rising heat of the blockchain concept, many enterprises compete to be the first to develop. However, the excessive enthusiasm of enterprises will inevitably lead to a large amount of waste of resources, therefore, it is particularly important to study the impact of the application of blockchain on the enterprise economy and finance. This paper first puts forward relevant assumptions on the behavioral characteristics of the participating subjects in the supplier order financing system, calculates the expected return function based on the return matrix and the initial ratio between the supplier, the company and the financial institution, and derives the replicated dynamic equation through further projection, according to which the evolutionary stabilization strategy is analyzed. Numerical simulation experiments are used to analyze how blockchain technology, receivable period, and guarantee rate affect the three-party gaming system. The concept of blockchain+supply chain finance model is proposed, and the method of empirical analysis is utilized to study the performance impact it has on enterprises. The thresholds of blockchain technology cost, accounts receivable period, and guarantee rate are [170,000 yuan, 190,000 yuan], [1.9a,2.0a], and [0.12,0.14], respectively. The operating revenue growth rate of the FT enterprise shows an increase in 2019 and 2020, which is 15.25% and 23.54%, respectively, and both of them are beyond the industry average in the same period, while the operating profit growth rate grows steadily and reached 102.54% in 2022, which can quickly respond to market changes and challenges.
Analysis of Artificial Intelligence-Based E-Commerce Transaction Anomaly Monitoring Strategies and Their Implementation Effects
Authors: Huang Yuqing , Nie Jialin
Published: November 2024

The rapid development of e-commerce is accompanied by the problem of a large number of abnormal transactions, and the development of artificial intelligence models has brought the possibility of large-scale real-time monitoring of transaction anomalies. The study integrates the transaction anomaly monitoring method based on the Light GBM model and multi-instance learning model, and trains the constructed combined model based on Taobao data. The average accuracy, precision, recall, and F1 values of the combined model are 0.977, 0.971, 0.973, and 0.972, respectively. The monitoring effect is higher than that of a single model, which indicates that the combined model is more effective in identifying anomalies in e-commerce transactions. The relevant technicians of A e-commerce platform who have used the model said that the implementation effect of the combination model constructed in this paper is more satisfactory.
Image Enhancement Network Architecture for Multidimensional Fusion of Medical Imaging Data under Intense Light Interference
Authors: Li Jiuhan
Published: November 2024

Due to the limitations of imaging equipment and environment, the acquired medical images usually have a certain degree of noise and artifacts, which leads to the degradation of the quality of medical images and affects the doctors’ clinical diagnosis of the condition. In this paper, the Gauss-Laplace operator is used to perform normalized filtering on medical images to reduce the influence of noise and improve the convolution effect of images. Through the CLAHE algorithm, the histogram is optimized for equalization, and the network architecture of the image is designed in this way. The quality of the enhanced image is evaluated through experimental design and dataset processing. In the evaluation of subjective and objective metrics, the PSNR and SSIM metrics of the images in SR × 2 are improved by 1.576 dB and 0.997 dB, respectively, on the BraTS dataset. This algorithm’s subjective score is the most high among the four enhancement algorithms, with an average score of 8.25, which aligns with the objective evaluation results. Among the image enhancement results, this paper’s algorithm better adjusts the histogram distribution with h(k) distribution ranging from 0.526-4.215, which is better than other enhancement algorithms in detail enhancement.
Research on intelligent identification algorithm for preventing external damage behavior of power cable based on machine learning
Authors: Li Qian , Guo Kang , Wang Siying , Zhang Jun , Zhang Zexin
Published: November 2024

As the key carrier of power distribution line power transmission and the supporting artery of urban functioning, power cables are widely used in electric power engineering construction. The number of users is increasing, and the distribution of locations is becoming more complex. However, if a power cable failure occurs, if not eliminated in time, it will threaten the safety of the power grid, seriously affecting the production of enterprises and the lives of residents. The article first on the power cable vibration signal recognition algorithm design, extraction of power cable external damage fault signal characteristics, proposed based on PSO-SVM power cable external damage fault waveform recognition algorithm, and the design of power cable anti-external damage intelligent early warning system, to achieve a certain period according to the trend of signal changes in the vibration event to discriminate. Research results show that under the same computer operating conditions, the average running time of the algorithm proposed in this paper is only 3.43. Compared to the other two algorithms, the algorithm has the highest recognition accuracy, the fastest convergence speed, and the optimal comprehensive performance. In the external damage signal recognition and warning test, the recognition rate of the excavator passing through defense zone one and two at a distance of 3 meters is 100%, which is greater than 80%, in line with the expected effect.
A Study on the Relationship between Digital Presentation of Intangible Cultural Heritage in Libraries and Visitors’ Interactive Experience
Authors: Liu Chunhui
Published: November 2024

Under the guidance of the theory related to the KANO demand model, this paper constructs the user demand hierarchy of the digital display of intangible cultural heritage and carries out the division of functional demand item categories through classification experiments. A questionnaire survey was set up to evaluate the satisfaction of the virtual reality technology of intangible cultural heritage crafts, and 30 participants’ interactive behaviors were coded so as to analyze the data results and explore the relationship between digital display and interactive behaviors. The findings indicate that both social and gamified interaction are charismatic needs of users when it comes to the digital display of NRM. In the practical case of virtual technology combined with non-heritage skills, the satisfaction level of design, content, and other aspects exceeded 60%. Non-heritage digital displays have 115 more interactive behaviors than traditional display methods. The interactive experience of participants can be enhanced and their needs can be satisfied by the digital display of NRLs.
Information flow and data-driven analysis of cross-cultural communication in the Chinese cultural community
Authors: Su Linting , Wang Cheng , Su Jian
Published: November 2024

The rapid development of the Internet also provides the possibility of information flow for cross-cultural communication, and the network not only breaks through the boundaries of time and space but also breaks through the limitations of cultural communication barriers. The study analyzes the influence mechanism of cross-cultural communication through the medium of social networks. A series of characteristics are proposed to characterize the flow of information, and a simulation experiment is carried out on the influencing factors of cross-cultural communication using the infectious disease model (SEIR). We utilize the Twitter dataset of real social networks to examine the effectiveness of the proposed SEIR model of information dissemination and verify its accuracy. The simulation experiment analyzes the influence of the containment mechanism, the time lag mechanism, and the forgetting mechanism on the model of information dissemination in cross-cultural communication, and information dissemination does not occur when R
0 <1. When R
0 >1, the cross-cultural communication information will be continuously and stably spread in the social network. This paper’s SEIR model has a positive effect on the dissemination of cross-cultural communication information, as evidenced by the results.
Research on the Influence Mechanism of Resilience of Agricultural Supply Chain Based on Mobile APP under the Perspective of New Development
Authors: Wu Tianjiao
Published: November 2024

The development of mobile apps provides a more convenient means of data collection, transmission and individual identification. The injection of new kinetic energy into the development of the agricultural supply chain is crucial for accelerating the construction of a modernized agricultural industry system. For this reason, this paper first analyzes the impact of the application of mobile apps in agricultural products enterprises on the output of agricultural products enterprises based on the theoretical model, and obtains fuzzy conclusions. Subsequently, the benchmark regression model was applied to examine the impact of mobile APP application on supply chain toughness, while the VAR model was used to study the direct effect of mobile APP application on the toughness of agricultural products supply chain and combined with the mediator model to verify the impact path of mobile APP application on the toughness of agricultural products supply chain. The model finally shows a positive response and gradual smooth convergence after giving shocks, indicating the positive effect of the development level of mobile APP applications on the toughness of the agricultural supply chain. The coefficients of the correlation between the level of trade development of mobile APP applications and innovation ability, human capital and supply chain resilience are all positive, indicating that the level of trade development of mobile APP applications has a significant positive impact on the resilience of agricultural supply chain, innovation ability and human capital. The investigation of the mechanism of the influence of mobile apps on the toughness of agricultural supply chains based on mobile apps has been completed.
Research on Visual Communication Design Strategies for Non-Heritage Cultural Creations in Shandong Yellow River Basin Based on Deep Learning Optimization
Authors: Yang Qiulu
Published: November 2024

As a brilliant treasure of human civilization, the non-legacy of the Shandong Yellow River Basin has always been an important carrier for people’s pursuit of beauty and unique expression. The rapid development of science and technology has resulted in the introduction of artificial intelligence and digital media technology, which have brought new vitality to cultural and creative visual communication design. The article introduces deep learning technology to analyze and extract non-legacy elements of the Yellow River Basin in Shandong, and deep learning technology mainly includes image recognition and image style migration technology. After studying the image recognition algorithm based on the migration of the generative adversarial model, the article proposes an image style migration model that integrates multi-scale discriminators, applies the decoupling training strategy so that the encoder is trained only when the adversarial loss is the largest, and finally sums up the visual communication design strategy optimized based on deep learning. Through the survey and study of 10 users’ satisfaction with the cultural and creative products designed by using the method of this paper, the total score rates of the four evaluation indexes, namely, content design, presentation design, element design and structural design, are 0.82, 0.83, 0.84 and 0.85, respectively, which the users highly praise. As a result, the visual communication design method proposed in this paper is effective for non-heritage cultural creations in the Shandong Yellow River Basin.
Research on Virtual Reality Art Creation Mode and Visual Perception under the Integration of Art and Technology
Authors: Zhang Nan
Published: November 2024

The use of cutting-edge information technology, such as virtual reality, has had a disruptive impact on the traditional model of visual perception art design. In this paper, we design a virtual reality modeling algorithm based on visual perception and QEM for creating virtual reality services. When the triangle feature constraint is combined with the QEM algorithm, it creates a triangle folding visual, geometric multi-feature metric function that guides the simplification order of the triangle face piece. Scene alignment and stitching using visual perception and QEM algorithms are examined, and the algorithms are employed to recreate the virtual reality of three selected artworks. This paper's algorithm achieves an average alignment rate of 90.93% for virtual scenes, outperforming the traditional algorithm by 15.64%. As for the quality of the scenes that were built, the mean standard deviation (SD) went from 11.92184 in the old way of doing things to 61.46286 in the new way. Similarly, the mean gradient (AG) went from 2.81938 to 15.58646, and the value of information entropy (EN) went from 5.40276 to 7.76728. Among the 12 dimensions scored by the experts for the first traditional artwork, only the light and shadow scored 51.13 points, while the rest were below 50 points. After the re-creation using the virtual reality technology in this paper, all the scores reached more than 50 points, with the highest being 88.8 points for visual aesthetics. With the exception of the background comparison, the virtual reality technology recreated the second work, improving the experts' scores on all dimensions, with the highest and lowest scores being 85.84 and 42.94 points, respectively. The third work also showed improvement in all aspects. In this paper, the algorithm optimizes the operation efficiency, reduces the number of rejections of the error transformation matrix and the running time of the model computation, and verifies that the virtual reality art creation mode can significantly improve the texture and level of artworks.
English Listening Prediction Strategies Based on Deep Learning and Ways to Cultivate Them
Authors: Luo Liang
Published: November 2024

This study proposes a strategy for predicting English listening learning performance and English listening achievement and constructs a corresponding model, which provides a basis for proposing a personalized cultivation pathway for English listening by implementing an English listening prediction model. The learners’ memory forgetting and learning behaviors are integrated into the Bayesian knowledge tracking model to obtain the BF-BKT model, and its corresponding CPT and Bayesian formula is updated to realize the prediction of English listening learning performance. With the help of the K-means algorithm to mine the relationship between learning behavior and learning performance, the gradient progressive regression tree in deep learning is used to analyze the connection between key influencing factors and performance, and the English listening learning performance is predicted. In this paper, the English listening learning performance prediction model ROC area AUC=0.826, and the relative error of the performance prediction model is always lower than that of the traditional model, with better prediction performance. Applying the personalized English listening cultivation pathway based on English listening prediction for English listening teaching, the total listening score of the experimental class was 36.88, which was higher than that of the control class by 5.62, showing a difference (P<0.01). The mean value of each specific strategy in English listening learning strategies was higher than that of the control class, and the overall listening teaching satisfaction reached 3.87, which was greater than that of the control class by 0.66.
Research on Mining and Analyzing Tourists’ Consumption Habits in Tourism Management
Authors: Ma Lijuan
Published: November 2024

The rapid development of the tourism industry has led to a continuous change in the way of tourism consumption. This paper takes the information of tourists’ consumption behavior as the research object and investigates their consumption habits. This paper adopts the fuzzy clustering (FCM) algorithm to analyze tourists’ consumption habits and clustering validity indexes and then uses the association rule algorithm on the basis of the FCM algorithm to mine the factors affecting tourists’ consumption habits in tourism management. In this paper, tourists are divided into five categories: free youth, couples, parents and children, families, and explorers. In tourism consumption, the top three primary concerns of tourists are “attraction characteristics, safety and consumption, which account for 30.34%, 18.04% and 12.07%, respectively. In the process of tourists’ tourism consumption, 93.47% of the concern factors are attraction features, with a confidence level of 98.76% and rule enhancement of 1.245, respectively. In addition, the probability of security and consumption appearing in the text at the same time is high, with rule support, confidence level, and enhancement of 89.52%, 75.59%, and 1.045, respectively. Attention should be given to the characteristics of attraction, safety, consumption, and service simultaneously. The results of this paper help to identify tourists’ consumption preferences and provide suggestions for tourism management centers to accurately understand tourists’ consumption habits.
Design of Real-Time Monitoring System for Tunnel Structure Based on Novel Sensor Technology
Authors: Meng Lingchen , Liu Yujia
Published: November 2024

This paper proposes a new wireless sensor tunnel monitoring system, comprising TAM sensors, a 5G network, a Zigbee network, and a monitoring center, based on the environmental characteristics of construction tunnels. The ZigBee algorithm enables the tunnel monitoring system to achieve real-time monitoring and fast transmission. We conducted detailed testing on the accuracy of the monitoring system, the validity of historical data, the response speed of the blasting control, the displacement, and the positioning accuracy, and analyzed the test results. According to the results, the new wireless sensor tunnel monitoring system has an accuracy rate of 94.79%, and the test value is comparable to the actual value. In the real-time monitoring of the stress of the support structure at the entrance and exit of the highway tunnel, a displacement ductility coefficient of 2.3 indicates the lowest bearing capacity, making it susceptible to damage. The T4 sensor was found to produce a maximum settlement of 1mm in the vertical direction during the highway tunnel blasting test.
A Strategic Study of Digital Marketing Techniques to Enhance the Attractiveness of International Tourism Destinations
Authors: Meng Zhihong
Published: November 2024

This paper studies the current level of attractiveness of international tourist destinations by constructing an evaluation system and applying a fuzzy comprehensive evaluation method. Based on the evaluation results, digital marketing strategies are proposed in terms of customer management management systems and social media marketing. Using the entropy value method and other comprehensive evaluation models to quantify the level of digital marketing and international tourism destination attractiveness, and with the help of the coupling degree coordination model to calculate the coupling degree of coordination between digital marketing and international tourism destination attractiveness. Combined with the fuzzy comprehensive evaluation results before and after the implementation of the digital marketing strategy and the change in international tourist destination traffic, the effect of digital marketing strategy on the attractiveness of international tourist destinations is analyzed in depth. Taking the Yangtze River Delta region as an example, this paper calculates the coupling degree between digital marketing and the attractiveness of international tourism destinations in 41 cities in the Yangtze River Delta region and concludes that the coupling degree between the attractiveness level of international tourism destinations and the level of digital marketing is high. Meanwhile, the fuzzy comprehensive evaluation score of City A in the Yangtze River Delta region and the passenger flow of 10 international tourist destinations between 2012 and 2022 increased significantly after adopting the digital marketing strategy, which fully indicates that the digital marketing strategy has a significant driving effect on the attractiveness of international tourist destinations.
Research on Human Resource Management Optimization and Decision Support System Construction Based on Electric Power Enterprise Strategy
Authors: Shen Haihua
Published: November 2024

At the present stage, the development of electric power enterprises needs to update the concept of human resource management by fully combining it with their actual situation and constructing a systematic human resource management model so as to occupy a favorable position in the market competition. This paper brings the classification rule mining algorithm based on the ant colony algorithm into human resource planning decision-making, adds an adaptive mechanism to the ant colony algorithm, updates the algorithm strategy, and improves the quality of algorithmic decision rules. Establish a fuzzy comprehensive judgment model for performance appraisal and salary decision-making in electric power enterprises and design a comprehensive system of dynamic decision support for human resource management in electric power enterprises. Analyze the effect of α, β and ρ values on the performance of the adaptive ant colony algorithm and determine the utility of the classification rule mining algorithm based on the adaptive ant colony algorithm for human resources planning and decision-making. Combine the evaluation level and scoring factors of employees in electric power enterprises to obtain the employee performance appraisal rankings and determine their remuneration packages. The practical benefits of the HRM dynamic decision support system designed in this paper are examined by a system reliability test. The reliability of the human resource management decision-making system designed in this paper is maintained at 0.93~0.97, and the decision-making reliability of traditional system 1 and traditional system 2 is maintained at 0.90~0.92 and 0.90~0.91, respectively. The decision-making system for human resource management in electric power enterprises constructed in this paper is more advantageous for practical operation.
Optimization of Civic Education Paths in Colleges and Universities with the Application of Intelligent Computing Models and Assessment of Their Effectiveness
Authors: Wang Jing
Published: November 2024

Teachers and students find it difficult to find subject-related curriculum Civics resources from massive learning resources. Combining recommendation algorithms with the curriculum for civics learning can solve the problem and optimize the path of civics education. The collaborative filtering algorithm is the preferred method to normalize the sample data in the first step of this paper, and then the scoring matrix is constructed to calculate student similarity to determine user preference. In order to solve the problem of user cold start, based on the K-Means clustering algorithm given the objective function, iteratively update until the center no longer changes or reaches the threshold value, and finally output the clustering results, you can realize the personalized recommendation of the Civic and political education resources, completed the design of the model. Koch’s four-level assessment model is used to assess the teaching effect of students under the recommendation model at four levels. The mean square error and average error of the proposed method in this paper are the lowest and the best performance in the comparison experiments, which illustrates the excellent performance of the recommendation model in this paper. Students are highly satisfied with the smart classroom based on the recommendation model of Civic Education Resources, and 84% prefer the teaching design based on the recommendation model of Civic Education Resources. The recommendation model also promotes the improvement of students’ grades, learning interests, and learning motivation. It shows that the proposed optimization path for Civics education is feasible.
Carbon Emission Reduction Target Setting and Implementation for Power Grid Enterprises Based on Intelligent Decision Support System
Authors: Wang Yan , Zhang Ruizhi , Qiao Yu , Wang Yanli , Wang Yunfeng
Published: November 2024

The electric power industry is the most important basic energy industry in the development of the national economy, which is now listed as a key energy-saving and emission-reduction field and is the main force in realizing low-carbon development. The study is based on the carbon emission accounting method, constructs a multi-objective decision-making optimization model with carbon emissions and economic benefits as the objective function, and decouples the model using the improved particle swarm algorithm. Then the TOPSIS algorithm is used to rank the optimal solution set of decision-making Pareto and select the optimal carbon emission reduction target for grid enterprises. Finally, the distributed integrated energy system of power grid enterprises under an intelligent decision support system is taken as an example for arithmetic simulation. The particle swarm algorithm and TOPSIS algorithm have the ability to determine the target for reducing carbon emissions in power grid enterprises, with the optimal economic cost and carbon emission ranging from 160.465 billion yuan and 467.865 million tons, respectively. It can be seen that at this time, the carbon emissions are relatively low and the cost is relatively low, which can meet the overall implementation of the carbon emissions reduction target of the grid enterprise. The study provides reference significance for the formulation and implementation of low-carbon emission targets for power grid enterprises, and promotes the sustainable development of power grid enterprises.
Data Mining Methods for Educational Effectiveness in Higher Education Aesthetic Education Programs
Authors: Wei Qidan
Published: November 2024

This paper describes in detail the commonly used educational data mining algorithms and proposes a new data mining-based teaching model for aesthetic education in modern universities. Data mining technology is used to collect students’ online learning behavior and learning performance data, and collate them to lay the foundation for improving personalized teaching work and teaching resource management in aesthetic education. To evaluate the effectiveness of the application of the modern education model implemented in colleges and universities, the rough set evaluation method was also introduced. The evaluation scores for the modern education model of the aesthetic education program in higher education are all above 0.8, and the rating scale is exceptional, as shown in the results. Compared with the previous stage, the students’ learning behavior input and knowledge mastery in the latter stage have been significantly improved, and the average score of declarative knowledge has increased by 27.42. The curriculum resources for calligraphy, drama, film, and television should be improved.
Analysis of Higher Vocational English Learners’ Behavioral Characteristics and Teaching Content Optimization Strategies Based on Big Data Mining
Authors: Wen Lulu
Published: November 2024

In the context of the big data era, the study of the massive data accumulated in the information construction of higher vocational colleges and universities can provide convenience for the teaching and management of colleges and universities. By improving the K-means clustering algorithm and Apriori algorithm in big data mining technology, the article mines the English learning behaviors and laws of higher vocational English learners and explores the correlation between learners’ behavioral characteristics and teaching performance. Finally, through empirical testing, this paper proposes an optimization strategy for teaching content in higher vocational English education. In the comparative analysis of reading and writing pre-test and post-test scores between the experimental class and the control class, the pre-test score of reading comprehension of the students in the experimental class is 31.25, and the post-test score is 32.84, and the average score of reading comprehension has increased by 1.59, which can be obtained that the English reading comprehension of the students has been improved after teaching with the teaching content optimization strategy proposed in this paper.
Personalized Learning Path Design for Civic Education Content in Colleges and Universities Based on Cognitive Computing
Authors: Zhou Yulin
Published: November 2024

This paper introduces the cognitive computing model into Civic Education in colleges and universities and collects the multi-dimensional cognitive data of the learners through methods such as eye movement data measurement sequence data mining, which accurately reflects the individual differences in the interaction in the educational environment. Using algorithms such as personalized learning to achieve personalized learning path design based on learners. Using 42 students from a university as the research subjects, it was found that students prefer video-type learning resources with approximately 62% of them choosing them. Cognitive characteristics are portrayed through students’ behavioral data: some students are very active in exchanging learning activities, while others are relatively independent. Students’ behavior showed a multi-linear pattern. There was a significant difference between the high and low wind groups in terms of assigning learning activities with a p-value of less than 0.05. There were outliers and inattentiveness in the eye movement trajectories of students 3, 12 and 5. Personalized teaching based on the cognitive computing model is significantly better than the traditional teaching model’s teaching effect.