Information Extraction and Spatial Pattern Analysis of Sports Events Based on Deep Learning

Abstract

The analysis of sports events information greatly promotes the formulation of scientific development strategy for sports events, the optimization of the spatial distribution of these events, and the popularization of sports culture. Most of the previous studies focused on the positive effects on sports events, but few quantified the effects. Besides, the broad environment of new media information communication was not considered. Therefore, this study carries out information extraction and spatial pattern analysis of sports events based on deep learning. Firstly, the texts of sports events information were classified by convolutional neural network (CNN), and the most valuable news or cases of sports events were screened. On this basis, a named entity recognition model was constructed to extract the most effective information from the data of sports events information. Next, a spatial pattern analysis approach was provided for the diffusion of sports event information flow. Finally, experiments were carried out to demonstrate the effectiveness of the proposed model and provide the spatial pattern analysis results on the diffusion of sports events information flow.