This study proposes an optimization architecture based on the deep forest algorithm to address the problems of low accuracy, slow response and poor adaptability of the traditional motion anatomy virtual simulation system. The algorithm learns multi-level features from motion anatomy data through the cascade forest mechanism, which effectively improves the accuracy of motion pattern recognition and the ability of anatomical structure association analysis. The system adopts end-to-end design and contains four core modules. The multi-source data acquisition module integrates inertial sensors, optical motion capture, and other types of signals. The feature extraction module utilizes multi-granularity scanning technology to generate multi-scale feature vectors. The deep forest processing module realizes layer-by-layer feature abstraction through cascade structure. The simulation and rendering module finally outputs highly realistic 3D visualization results. Experiments show that the optimized system significantly outperforms the traditional method in key indexes such as motion recognition accuracy, joint angle prediction, muscle activation pattern classification, etc., and at the same time, it also performs better in terms of real-time performance and system robustness.
