Physical fitness evaluation system for athlete selection based on big data technology

Big data has developed into an important emerging industry. This research mainly discusses the design of the physical fitness evaluation system for athlete selection based on big data technology. The system consists of evaluation indicators, indicator weights and evaluation standards. According to the principles of data mining, Apriori algorithm and FP algorithm, based on the index of physical data and combined with the algorithm principle, this paper determines the minimum support of the association rules, thereby establishing the association model between the physical fitness test level and the individual. In the experimental test, the athletes' 10-metre turn running mainly concentrated in 2.0-2.5 seconds, accounting for 58.0% of the total. The research concludes that the physical fitness evaluation system for athlete selection designed in this article has a certain degree of scientificity and good performance.