The explosion of digital technology and the Internet has elevated big data as a critical driver of progress in various fields, including sports education in higher education institutions. This article explores the application of structured data mining to refine sports education, beginning with a decision tree algorithm for student sports data analysis. It then employs the Apriori algorithm to explore gender-based sports information correlations with teaching levels and the K-means algorithm to measure the enhancement in sports teaching quality pre and post-technology adoption. Findings reveal a strong association between improved teaching quality and student physical well-being, highlighted by the College of Physical Education’s top teaching quality score of 10.0. Initially, most teaching quality evaluations were in the “poor” to “good” range (81.2%), shifting significantly to “excellent” and “good” (78.4%) after the intervention. This study evidences the importance of data mining in revolutionizing physical education, significantly boosting educational quality.