Sports Risk Analysis Based on Knowledge Discovery and Data Driven

Abstract

Sports have gradually gained popularity, and the risks associated with them have risen as well. In today’s world, college students are a diverse population, and sports are very popular among them. The construction of sports risk assessment system in ordinary colleges is significant to improve physical quality of college students and ensure safety. College sports accidents have occurred on occasion in recent years, causing not only enormous pain to students and parents, but also casting a dark shadow over the sport. This work takes the risk analysis of college students’ sports as the background, and uses a data-driven neural network to conduct knowledge discovery of college sports threats. It evaluates the sports risks of college students’ physical education, and builds an index system of sports risk assessment in ordinary colleges and universities, which provides a certain basis for avoiding and reducing sports risks. This work studies an end-to-end one-dimensional convolutional neural network algorithm for risk assessment of college sports. In order to extract complementary structures in different scales, a multi-scale fusion framework is constructed using convolution kernels of diverse sizes. In this paper, the residual network structure is introduced to deepen and improve the network, and an attention module suitable for one-dimensional residual network is designed. It is embedded into the residual module to construct a multi-scale attention residual network (MSAR) model. Finally, validity and superiority of proposed model are verified by experimental data, which can effectively evaluate the sports risk of college students.