Intelligent Algorithms for College Physical Education Athlete Training Using Computer Big Data Technology

Abstract

In order to solve the problems of long training content mastery time, poor training effect, and high cost in traditional training systems, the author proposes the use of computer big data technology in the research of intelligent algorithms for athlete training in university physical education. The author begins by gathering sports data from athletes, then utilizes a virtual reality perception interaction model to feed the data into the virtual environment generation unit. Data fusion is performed under the supervision of the simulation management module. Finally, combined with the motion behavior interpretation in the database and image card of the sports simulation training data unit, simulation 3D modeling is carried out in the 3D model processor according to user settings. The experimental results demonstrated that athletes trained with this system (the third group) had a markedly better understanding of the training content compared to those trained with the other two systems (the first and second groups) after the first week, achieving a mastery rate of 73.875%. As the duration of the training increased, all groups showed improved mastery of the content. This system enhances athletes’ training effectiveness while also reducing training costs, offering significant practical application value.