The enhancement of physical education’s stature necessitates imminent reforms in university physical education models. This study examines the deployment of an intelligent motion-tracking system within physical education, exploring facets such as the precise capture and real-time analysis of movement postures, movement evaluation, and optimization, as well as movement recording and tracking. It further investigates the system’s accuracy and convenience, assessing its impact on enhancing university physical education pedagogy. Our methodology involved constructing a framework for the intelligent motion tracking system, followed by developing a motion model. The design of the intelligent tracking model proceeded through initialization, sampling and prediction, observation, and resampling phases, concluding with the implementation of a complete human body tracking motion algorithm utilizing the VHMT algorithm. Our analysis revealed optimized posture and morphology of the upper arm and forearm during exercise, indicative of enhanced physical education teaching at the university level. Prior to implementation, in the volleyball class scenario, no significant difference was observed between the experimental and control groups in terms of motor skills, with a difference ranging from 1.82 to 4.4 (P>0.05). Post-implementation, however, the motor skill level of the experimental group in each class significantly surpassed that of the control group (P<0.05), with improvements ranging from 6.8% to 43.4%. The integration of an intelligent motion-tracking system in university physical education substantially fosters students’ motor skill acquisition and invigorates their enthusiasm for learning.