Journal of Multimedia Processing and Technologies Zhenzhen Yun 17 1 2026 https://doi.org/10.6025/jmpt/2026/17/1/21-33 https://www.dline.info/jmpt/fulltext/v17n1/jmptv17n1_3.pdf The paper proposes an integrated approach for analyzing human movements using fuzzy clustering combined with deep learning techniques. It addresses challenges in traditional computer vision methods such as occlusion, motion blur, and lighting variations that hinder accurate player tracking and posture recognition in dynamic match environments. The proposed methodology involves preprocessing video frames to reduce noise, followed by fuzzy clustering based image segmentation to distinguish players from the background. Deep learning is then applied for feature extraction, posture identification, and motion tracking. The fuzzy Cmeans (FCM) algorithm plays a central role, offering soft clustering that accommodates ambiguous boundaries and overlapping actions. The paper reviews related work, highlights FCM's strengths and limitations, and introduces enhancements, including residual-driven FCM and contextual clustering, to improve noise robustness. Experimental validation using university human data demonstrates that the model effectively quantifies movement accuracy and supports performance improvement through targeted training feedback. Results show a measurable increase in motion precision over three months, confirming the model's practical utility. The study concludes that fuzzy clustering, especially when fused with deep learning, provides a robust framework for human motion analysis in sports, offering coaches and athletes actionable insights for performance evaluation and tactical refinement.