With the swift progression of machine learning technology, its potential applications in education are vast. This study introduces a convolutional neural network (CNN)-based algorithm designed to evaluate the quality of reforms in physical education classroom teaching. The goal is to improve the effectiveness and impact of physical education instruction through technological advancements. The research initially scrutinizes the limitations associated with traditional methods of evaluating the quality of physical education classrooms and delves into the possibilities afforded by employing CNN for assessing teaching quality. By formulating and implementing a CNN model tailored to the context of physical education classes, this study adeptly analyzes real-time student movement patterns, participation levels, and interaction effects. The experimental outcomes demonstrate the algorithm’s effectiveness in discerning critical teaching elements within the classroom, including students’ enthusiasm for engagement in activities and teachers’ instructional methodologies. The algorithm enables a quantitative assessment of these components. Additionally, this research explores the practical implementation of the algorithm in teaching contexts, including providing real-time feedback to educators and adjusting teaching strategies based on assessment outcomes. The results offer a fresh perspective for enhancing the quality of physical education instruction and lay the foundation for the wider adoption of machine learning technologies in the education sector.