Research on Evaluation Model of Physical Education Learning and Training Effect of College Students Based on Data Mining Technology

The experience of organizing and managing activities in the traditional sports industry has been introduced into university sports, enabling universities to organize various sports competitions, sports events, fitness activities, etc. more efficiently. By drawing on the management model of industrialization, university sports can ensure the smooth progress of activities while enhancing student participation and satisfaction. Modern university sports not only focus on the construction and improvement of sports facilities, but also actively introduce intelligent sports equipment and fitness apps to provide students with more convenient and personalized sports learning and training experiences. The integration of these products and services makes university sports more closely aligned with the actual needs of students. The research on sports industry innovation is also deepening. Therefore, the research on the construction of university sports sustainable development strategic goals is based on data mining algorithms. By summarizing the concept of the traditional sports industry, according to the superiority, the relevant data are processed. The evaluation model of physical education learning and training effect of college students is based on data mining technology. By analyzing students’ sports learning and training data, the hidden information and knowledge can be mined to provide effective evaluation and guidance for college sports teaching and training. This study will use data mining technologies, such as decision trees, support vector machines, neural networks, etc., to conduct in-depth analysis of students’ sports learning and training data, and find out the impact of learning, training, etc. The key factors of the training effect and the corresponding evaluation model are constructed.