THE EARLY WARNING MODEL OF TRACK AND FIELD SPORTS BASED ON RBF NEURAL NETWORK ALGORITHM

Background: Athletics plays a very important role in competitive sports. The strength of track and field directly represents the level of a country's sports competition. Objective: This work aimed to study the track and field sports forewarning model based on radial basis function (RBF) neural networks. One hundred outstanding athletes were taken as the research objects.The questionnaire survey method was adopted to count athletes' injury risk factors, and coaches were consulted to evaluate the questionnaire's overall quality, structure, and content. Methods: A track and field early warning model based on RBF neural network is established, and the results are analyzed. Results: The results showed that the number of people who thought the questionnaire was relatively complete (92%) was considerably higher than that of very complete (2%) and relatively complete (6%) (P<0.05).The number of people who thought that the questionnaire structure was relatively perfect (45%) was notably higher than that of the very perfect (18%) (P<0.05). The semi-reliability test result suggested that the questionnaire reliability was 0.85. Tests on ten samples showed that the RBF neural network model error and the actual results were basically controlled between -0.04 similar to 0.04. Conclusions: After the sample library test, the track and field sports forewarning model under RBF neural network can obtain relatively favorable results.