Study on the Sustainable Development Strategy of School Soccer Based on the Background of Big Data Era

Abstract

Big Data is the most popular concept in this era, which is the massive amount of information and related technology generated by the information explosion in the era of “Internet+.” Big Data is the most popular concept of our time. With the most advanced technology to collect, analyze, organize, and store data, Big Data can effectively handle all kinds of complex information. Because of this, big data is widely favored by all walks of life. In China’s sports industry, the use of big data has become mature and has shown its unique advantages. With the development of campus soccer in China in the past decade, how to use big data to promote the sustainable development of campus soccer in China has become a key issue for sports workers to consider today. Based on the above background, this paper proposes a system combining data mining and personalized data recommendation to collect and analyze the information of campus soccer to promote the sustainable development of campus soccer. First, we propose a data mining method based on deep learning data mining network model combined with migration learning to address the data mining problem. The method uses the knowledge of historical model parameters and applies them to new tasks, thus solving the problem of network training when samples are lacking and improving data utilization and data mining effects. Then, for the data recommendation problem, a new deep learning method is proposed, which performs effective intelligent recommendation by pretraining. In the initial phase, the corresponding low-dimensional embedding vectors are learned, which capture information reflecting the relevance of students to soccer sports. During the prediction phase, a feed-forward neural network is used to model the interaction of student and soccer sport information, where the corresponding pretrained representative vectors are used as inputs to the neural network. Finally, it is experimentally verified that the data mining method proposed in this paper can effectively improve the data mining performance and efficiency, and the proposed data recommendation method possesses better accuracy than the traditional methods. The use of this system can effectively collect and analyze campus soccer information, which helps to develop campus soccer and promote the sustainable development of campus soccer.