Knowledge Graph-Based Badminton Tactics Mining and Reasoning for Badminton Player Training Pattern Analysis and Optimization

As the global emphasis on sports data analysis and athlete performance optimization continues to grow, traditional badminton training methods are increasingly insufficient to meet the demands of modern high-level competitive sports. The exploration and reasoning of badminton tactics can significantly aid coaches and athletes in better comprehending game strategies, playing a vital role in the analysis and optimization of training methods. By utilizing knowledge graph-based badminton tactics mining, an approach involving heterogeneous graph splitting is employed, coupled with the incorporation of a cross-relational attention mechanism within relational graph neural networks. This mechanism assigns varying weights based on the importance of neighboring nodes across different relations, facilitating information aggregation and dissemination across multiple relationships. Furthermore, to address the challenges posed by the complexity of large-scale knowledge graphs, which feature numerous entity relationships and intricate internal structures, techniques such as training subgraph sampling, positive-negative sampling, and block-diagonal matrix decomposition are introduced. These techniques help to reduce the computational load and complexity of model training, while also enhancing the model's generalization capabilities. Finally, comparative experiments conducted on a proprietary badminton tactics dataset demonstrated the effectiveness and superiority of the proposed model improvements when reasonable parameters were applied. The case study shows that this approach holds considerable promise for the analysis and optimization of badminton players' training strategies.