<p>This paper aims to simulate and analyze the movement process of football players through data mining algorithms based on wearable thermal sensors, combined with network data security technology. With the global popularity of football, the volume of related data has grown explosively, and the issue of network data security has become increasingly prominent. Risks such as data leakage and tampering threaten players’ privacy and the team’s business secrets, and may also affect the accuracy of game result analysis and tactical decisions. To this end, this paper proposes a set of data mining algorithms, which use wearable thermal sensors to collect real-time thermal radiation images of athletes, and analyze their physical energy consumption, fatigue degree and exercise efficiency. At all stages of data collection, transmission, storage and processing, network data security measures such as encryption technology, identity authentication, access control and data desensitization are adopted to ensure the integrity, confidentiality and availability of the data. The experimental results show that this algorithm has a high accuracy rate in simulating athletes’ physical energy consumption and fatigue degree. Compared with traditional monitoring methods, it has more advantages in predicting athletes’ efficiency. The effective application of network data security technology ensures the reliability of the data mining process and provides strong support for the scientific formulation of football training plans and the optimization of match strategies. The research in this article provides new perspectives and tools for the digital development of football, and also offers practical cases for the application of network data security in the sports field, which is conducive to promoting the development of football towards a higher level and greater security.</p>