In education, it is crucial to comprehensively evaluate the physical fitness of students. The limitations of information processing and analysis efficiency make it difficult for traditional evaluation methods to reveal deep physical correlation patterns. Given this, this study will focus on innovative evaluation methods that combine frequent pattern growth algorithms. An Apriori association rule model based on transaction compression and hash optimization is proposed to address association classification between physical fitness indicators. Moreover, this study optimizes operational efficiency through preprocessing techniques and hash acceleration strategies. It introduces enhancement parameters to accurately identify and establish strong association rules to achieve efficient and accurate evaluation of student physical fitness. The results showed that by comparing the running time of K-means + FP-growth and improved FP-growth under different support levels, the improved FP-growth tended to stabilize after a support level of 0.2%. The optimized model improved the execution efficiency by 82.87%–88.4% compared to Apriori and FP-growth in physical measurement data processing. The effectiveness and reliability of the improved algorithm were verified by measuring strong association rules with the introduction of enhancement degree. This study is expected to better understand the physical fitness status of students, and provide new ideas for educational decision-making and practice, which has profound practical significance for promoting innovation in physical fitness assessment methods.