Analysis and Evaluation of Sports Effect Based on Random Forest Algorithm under Big Data

Under the background of big data, all walks of life have carried out in-depth informatization construction. As an important part of national education, the informatization construction of universities cannot be ignored. In recent years, the state has promulgated various policies and regulations, which provide a guarantee for the normal development of school physical education and can improve the current situation of college students' declining physical health to a certain extent. This study attempts to set some specific indicators to promote the better implementation of college sports so that it can supervise and inspect them from all aspects during the actual development of college sports and provide a standard for measuring the implementation effect of college sports. Based on the RF (Random Forest) algorithm, this paper puts forward an evaluation algorithm of students' sports achievements, which can be used to predict students' sports achievements and at the same time, find out the factors that affect students' learning and rank them in importance. The results show that the confidence level of sports effect evaluation by this method is high, and the average confidence level is above 0.96. Conclusion. This method has improved the effect of sports effect evaluation, thus effectively guiding sports skills training and improving sports skills.