Integrating the Big Data in Sports and Resource Interaction Using Artificial Neural Network

Abstract

Big data is the result of balancing computing power with the need for large, rapidly updated, and comprehensive datasets and is now widely used in urban planning, medicine, and other fields. With the advent of the era of big data, the sports industry must also adapt to this change. The era of big data brings new ideas to the development of sports resource interaction. At the same time, the demand for a high-efficiency and high-performance sports big data integration system is imminent. The interaction between college sports and community sports realizes the improvement of the practical ability of the two institutions and departments through the satisfaction of people's own needs. And it affects people's lifelong sports and health by improving external conditions to internal factors. It improves the quality of life and maintains social stability and peace, thereby ensuring the better development of the university and the community. At present, the speed of data integration in the sports big data integration system is relatively slow. In order to solve this problem, this study introduces BP neural network in the artificial neural network. It introduces the concept of the neural network and its related formulas in detail. By using the BP neural network classification algorithm for data classification integration, this study conducts training and performance testing of the network. It links the extraction, transformation, and loading of data. The experimental results show that when the number of databases is 2, 4, 6, and 8, the time taken to load data by the system in this study is 113 s, 87 s, 64 s, and 42 s, respectively. It can be seen that the system in this study has obvious advantages in data loading compared with the data integration system of the existing system data warehouse. The system data query designed in this study is more efficient and consumes less time.