Mining and Analysis of Technical Actions in Basketball Matches Based on Network-Integrated Teaching

This paper focuses on the mining and analysis of technical movements in basketball games under the background of integrated network technology teaching. In today’s rapidly changing network technology, the basketball teaching system is undergoing unprecedented innovation and transformation. This paper specifically explores how to deeply explore and analyze the correlation and regularity of technical movements in basketball games based on the framework of network-integrated teaching. In order to gain a deeper understanding of the correlation between technical movements in basketball games, this paper originally planned to conduct in-depth mining from an algorithmic perspective. However, in this revised abstract, we focus more on the role of network-integrated teaching in this process. Through the network-integrated teaching platform, basketball coaches and students can watch game videos in real time, use the analysis tools provided by the platform to annotate, classify, and statistically analyze technical movements, and discover the regularity and correlation of technical movements. This paper also explores how to identify and analyze the biomechanical characteristics of basketball players in the context of network-integrated teaching. Although the specific algorithm details are not elaborated in this abstract, we emphasize the importance of using online platforms for data collection, processing, and analysis, as well as the potential value of these processes in improving the accuracy of basketball technique action recognition.