Analysis and Research of Athletes’ Physical Fitness Test Index Based on Data Mining Technology

Abstract

The physical fitness test index is an index to measure the physical fitness of athletes. It is used to evaluate the performance of athletes in various events. The purpose of this study is to analyze and study the physical fitness test indicators of athletes based on data mining technology. Research methods This study adopts a combination of qualitative and quantitative methods. Researchers used primary and secondary sources in this study. Research method This research method is a combination of qualitative and quantitative methods. This study uses various sources, such as literature review, primary and secondary data collection, interviews, observations, etc. Build a fast association classification model of physical fitness indicators based on improved data mining algorithm to find out the association between physical fitness indicators. The physical fitness evaluation model can efficiently analyze the health level of athletes' physical fitness, providing a new research perspective for physical education and evaluation of physical fitness level. This article takes the analysis of athletes' physical fitness test data as the research object, and studies the principles and methods of data mining technology to solve the problems in the management and analysis process of athletes' physical fitness test indicators. This paper introduces the basic principles, methods, and processes of data mining technology, with emphasis on the principles, methods, and typical algorithms of association rule mining and neural network mining. An algorithm suitable for analyzing tennis players' physical fitness data was proposed and implemented, and applied to the database, discovering unconventional rules.