Internet Data Risk Model of Sports Events Based on Convolutional Neural Network
Lin Song · 2022
In the field of new media and big data, sports events and news media need to be integrated and promoted, and there are more and more sports information, especially sports information on the Internet. The purpose of this paper is to study the risk model of sports event Internet data based on convolutional neural network. A progress label prediction network based on ResNet-50 is designed to obtain risk label sequences of test videos. A sports event action dataset from the first perspective is established. Experiments on this dataset show that the sports event internet data risk model proposed in this paper can achieve high-precision action localization. Comparing the results with the three traditional methods, found that compared to 85% of BP, the model in this paper is more effective in predicting the risk of Internet data in sports events. There is a certain improvement in the accuracy of the model, which fully demonstrates the effectiveness and practical significance of the model.