Against the backdrop of the coordinated advancement of educational digital transformation, the Healthy China Initiative, and the high-quality development of university physical education, public physical education in universities urgently needs to move beyond traditional teaching models characterized by standardization, experience-driven decision-making, and outcome-oriented assessment, and adopt a precision-oriented educational model characterized by data-driven support, individualized adaptation, and continuous improvement. Smart Sports should not be regarded merely as conventional information technologies but as a comprehensive instructional support system that reshapes the operational logic and decision-making mechanisms of physical education by integrating data, intelligent analysis, and feedback regulation. Precision-oriented public physical education in universities emphasizes the utilization of multi-Source data, including student physical fitness, sports skills, classroom participation, extracurricular exercise, and health behaviors, to achieve accurate identification of student learning profiles, accurate diagnosis of instructional problems, precise alignment of instructional content, adaptive regulation of instructional processes, and adaptive regulation of instructional processes. Currently, university public physical education still faces challenges such as data fragmentation, experience-dependent diagnosis, homogenized instructional support, lagging evaluation mechanisms, and deficiencies in data governance and teachers' digital literacy. To address these issues, a closed-loop framework of “data collection–student profiling–intelligent analytics–instructional support–evaluation and feedback” should be established to promote the transition of public physical education from experience-based decision-making to data-informed, evidence-based practice, from standardized instruction to personalized support, and from outcome-focused assessment to process-oriented continuous improvement.