Prediction of the Number and Trend of Olympic Medals Won by Various Countries Based on Neural Networks

Abstract. As the world's largest multi-sport event, the Olympic Games attract top athletes from all over the world to compete for the world No. 1 crown. The total number of medals in each Olympic Games is the most intuitive data to measure the sports strength of a country. Using deep learning technology, the total number of medals in previous Olympic Games can use to predict the number of medals that countries will receive in future Olympic and analyze their trends,which is helps for Countries understand the strengths and weaknesses of their athletes in various sports, and also provide a reliable reference for countries in the future development direction of sports .Based on the analysis of the total number of medals won by athletes from various countries in the Olympic Games, a deep learning model is constructed and trained based on Python, and its hyperparameters are adjusted. The mean square error (MSE) and coefficient of determination (R) are output as the evaluation criteria for the prediction results. The effectiveness and accuracy of the model were verified by making specific predictions of the previous Olympic performance of four countries: United States, Germany, China, and Japan.Medal prediction based on neural networks not only provides data support for the formulation of sports strategies in various countries but also promote the development of sports data analysis. The prediction model of the change trend of the Olympic medal count will provide a new research perspective for the development of neural networks.