Analysis of Big Data Behavior in Sports Track and Field Based on Machine Learning Model

Abstract

At present, machine learning is more efficient and accurate for the efficiency of operation logic after four stages of reform. In order to improve the participation rate of the whole people in track and field sports and get a better level and ranking in track and field competitions, the ATI model under the machine model is used to deeply analyze the behavior of track and field sports in order to get more accurate data. There are a series of problems in the process of correlation analysis, such as the loss caused by the analysis process, the error in the analysis process, and the lack of understanding of track- and field-related data. In order to solve this series of problems, this study optimizes the behavior analysis through related experiments. The experiment proves the correlation between learning rate and loss. When the learning rate is 0.1, the loss caused by behavior analysis is lower. For the 23rd–28th session, the number of gold medals and the number of medals won in track and field were analyzed. By comparing the ATI model with the ATT model, ATT-Net model, and WAT model, it is concluded that the ATI model has a lower error rate for behavior analysis under big data. The coverage rate of behavior analysis data is wider. Therefore, in order to make track and field behavior analysis more accurate and stable under big data, the ATI model under machine learning should be preferred for data collection, collation, analysis, and summary. Through the ATI model to analyze the related behavior of track and field under big data, there are the following advantages: when the learning speed is 0.1, the loss value in the analysis process is reduced; the number of neurons is increased, and the dropout rate is reduced to reduce NPMSE value; and the error loss rate of behavior analysis is reduced, and the analysis coverage rate is increased.