Application of a Multi-Strategy-Based Visual Decision Support Method in Shooting Sports

Traditional shooting performance analysis and decision-making rely heavily on manual statistics, which is time consuming, laborious and inefficient. The rise of artificial intelligence and big data has revolutionized decision making and inspired us to apply smart data-driven decision making to shooting sports. By adopting a multi-strategy methodology, we address the long-term challenges that elite shooting teams face in making decisions quickly utilizing available data, while introducing intelligent decision-making tools that visualize complex data into intuitive patterns and trends through 2D and 3D visualization, systematically compute key indicators, and render them legitimately in graphical format. We developed athlete performance evaluation functionality using the advanced DeepSeek large model tool. Based on data model, the XGBOOST algorithm in combinatorial learning and ARIMAX algorithm in statistics are used to realize the comparative prediction models of elite athlete performance, and the excellent model in unique shooting achievement dataset is determined. This achievement maximizes the effectiveness of using available data for performance analysis and decision-making support.