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During college physical training, a substantial amount of data is continually generated through various activities. Historically, technological constraints have hindered the effective collection and utilization of this data, limiting its potential to enhance physical education (PE) and training through intelligent support. This study explores the application of data mining (DM) in managing and analyzing PE classrooms. It aims to streamline PE teaching management and evaluation, conduct in-depth analyses of students’ physical fitness data, and ultimately elevate the quality of school PE instruction. In designing a big data system for this purpose, data collection is prioritized based on its difficulty. To facilitate data analysis and presentation, a high-performance framework for storing and analyzing data is selected. The results indicate a 13.28% improvement in accuracy compared to traditional assessment models. By leveraging DM in PE classroom assessments, we can mitigate subjectivity and uncertainty associated with manual weight and correlation coefficient selection. This approach enhances the intelligence, adaptability, and usability of the assessment model, paving the way for more effective PE teaching and learning.

Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation. [ABSTRACT FROM AUTHOR]

In recent years, national and local governments have attached great importance to the development of tourism big data, and have proposed an active development of the 'Internet + Tourism' strategy to cultivate new forms of tourism in various forms. With the development of 5G technology, the tourism industry also needs to study new development paths. The traditional tourism industry cannot obtain relevant information of tourists on time, nor can it provide satisfactory services, which has brought troubles to the traditional tourism industry. However, with the support of 5G technology, smart tourism is increasingly meeting the needs of the public and improving the level of tourism services. Based on this, this paper proposes a new model path research for the development of the smart leisure sports tourism industry based on 5G technology. This paper adopts the literature method to learn the theoretical basis of 5G technology and smart tourism in depth; through 5G technology, establishes a multi‐dimensional resource allocation model for the smart leisure sports tourism industry, and conducts research on the influencing factors, information sources, channel factors and other aspects of the tourism industry. The general public's search for tourism strategies and attractions, food and specialty products, the use of online search information channels accounted for 70.3% and 69.3%, which further shows that the development of 5G technology has promoted the transformation and development of the sports tourism industry. [ABSTRACT FROM AUTHOR]