Basketball Training Movement Pattern Recognition and Personalized Teaching Strategy Design Based on Multidimensional Data Analysis

Abstract

Basketball is a sport with a broad grassroots following, and its scientific research value has also garnered significant attention. Currently, most basketball training content is based on coaches' experience and intuition when training athletes, lacking unified standards and personalized guidance. To address the issues of disorganized data and unclear objectives during training, this paper proposes a method that integrates a dual-stream convolutional neural network with a long short-term memory network, based on video analysis techniques, motion trajectory tracking, and knowledge graph construction technology. It also employs a diffusion probability model for player synthesis, a Monte Carlo optimization model for shot strategy decision-making, and multi-object tracking technology to resolve the issue of mutual obstruction between players, the ball, and the court during games. Through actual testing, it was found that the model and algorithms constructed in this paper not only improve training effectiveness but also provide personalized, targeted technical improvement suggestions for different individuals. By fully utilizing mechanical analysis and motion trajectory tracking methods, as well as machine learning and computer vision algorithms, this study enhances the scientific nature of basketball training and provides new insights and methods for technical analysis research and training content development in other sports fields.