Abstract This study aims to use the intelligent particle swarm algorithm to optimize the development model of traditional ethnic sports. By analyzing the characteristics and problems of the development of conventional ethnic sports, combined with the advantages of the particle swarm algorithm, an optimization model was constructed, and an empirical study was carried out. The model for applying the particle swarm algorithm to optimize... This study aims to use the intelligent particle swarm algorithm to optimize the development model of traditional ethnic sports. By analyzing the characteristics and problems of the development of conventional ethnic sports, combined with the advantages of the particle swarm algorithm, an optimization model was constructed, and an empirical study was carried out. The model for applying the particle swarm algorithm to optimize the development of traditional ethnic sports was proposed. In the model construction, the development problems of traditional ethnic sports were transformed into an optimization problem. Using the particle swarm algorithm’s search and optimization capabilities, the model was solved and the optimal decision scheme was obtained. The intelligent particle swarm algorithm can scientifically and effectively solve the problems faced by the development of traditional ethnic sports. Read More Read Less

Abstract The development of big data technology has driven the pace of teaching innovation and reform. In the information age, education emphasizes personalized and comprehensive development of students more than ever before. This paper combines big data algorithms to construct an intelligent education model for classroom student profiling. The model leverages big data mining algorithms to discover the correlations in student behavior data. Using classification... The development of big data technology has driven the pace of teaching innovation and reform. In the information age, education emphasizes personalized and comprehensive development of students more than ever before. This paper combines big data algorithms to construct an intelligent education model for classroom student profiling. The model leverages big data mining algorithms to discover the correlations in student behavior data. Using classification algorithms based on multi-frequency patterns, the model classifies student behavior data and constructs multi-frequency pattern trees for students with different academic performance, reflecting differences in their learning behavior characteristics. Experimental results demonstrate that applying the intelligent education model based on big data algorithms can effectively provide teachers with comprehensive and accurate feedback on student behavior characteristics, helping students understand their learning situations and enabling targeted personalized teaching, significantly improving students’ learning quality and efficiency. Read More Read Less

Abstract In today’s higher education institutions, the development of “big ideology” has become essential. From a macro perspective, integrating “big ideology” into the classroom allows for better utilization of course resources and improves the classroom atmosphere. It also makes the curriculum more relevant to students’ daily needs, thereby better cultivating their moral qualities. Therefore, this study explicitly analyses the university education model of nurturing students... In today’s higher education institutions, the development of “big ideology” has become essential. From a macro perspective, integrating “big ideology” into the classroom allows for better utilization of course resources and improves the classroom atmosphere. It also makes the curriculum more relevant to students’ daily needs, thereby better cultivating their moral qualities. Therefore, this study explicitly analyses the university education model of nurturing students from the ideological and political education perspective, considering the association rules algorithm and its value. The study optimizes the association rules algorithm, drawing inspiration from the harmony search algorithm’s main ideas, and introduces the concept of “self-learning” for students’ improvement, thereby enhancing the overall search capability of the group and delving into the deficiencies in the analysis of students’ existing course grades. The study proposes an association rules algorithm with self-learning capabilities, and the optimized performance of the association rules algorithm surpasses that of the function algorithm. The comparative results indicate that the optimization can achieve the ideal optimal solution of the reference function, and the proposed optimization significantly improves the computational performance of the rules algorithm. The algorithm’s advantage increases considerably from 1000 rounds, and the network’s lifetime extends to 1600 rounds. Through testing, it is found that applying association rules mining technology to analyze students’ academic performance helps identify the relationship between course settings and course grades on the platform network, contributing to discovering essential information hidden in the network. Universities should actively explore the model of ideological and political education in practice and cultivate students, construct a “dual-platform and four-linkage” practical education model considering the association rules algorithm under the perspective of “big ideology,” fully exert the value of ideological and political education in practice, and promote effective reform and progress of ideological and political education in universities. Read More Read Less

Abstract Red tourism is a form of tourism with red tourist attractions and related historical culture as its theme. It is significant for promoting historical and cultural preservation and economic development of tourism. The configuration of the government’s red tourism policy tool combination is crucial for developing red tourism. This study explores how to effectively configure the combination of red tourism policy tools to... Red tourism is a form of tourism with red tourist attractions and related historical culture as its theme. It is significant for promoting historical and cultural preservation and economic development of tourism. The configuration of the government’s red tourism policy tool combination is crucial for developing red tourism. This study explores how to effectively configure the combination of red tourism policy tools to achieve sustainable development of red tourism based on the K-means clustering algorithm. Firstly, red tourist attractions are analyzed and clustered into different categories. Then, combining relevant historical and cultural data and tourism economic data, the K-means clustering algorithm is applied to optimize the configuration of red tourism policy tools. Through systematic research, we found that the configuration of red tourism policy tools using the K-means clustering algorithm can effectively meet the needs of different categories of red tourist attractions, thus promoting the sustainable development of red tourism. This research provides a valuable reference for the government and relevant tourism agencies in configuring red tourism policy tools. Read More Read Less