<p>Wearable sensor devices are connected to cloud databases via networks, transmitting users' biosensor data and movement trajectory data in real time. These data involve users' personal privacy and health information. Once leaked, they may cause serious harm to users. Therefore, this study aims to explore the application of a national fitness data monitoring system based on wearable sensor technology and POI recommendation algorithms. The research adopts the B/S three-tier architecture model to design the system, including the user interface layer, business logic layer and data access layer. By collecting users' biosensor data and movement trajectory data through wearable sensors and combining them with POI recommendation algorithms for data processing and analysis, personalized fitness suggestions and training plans are provided for users. In terms of network security, multi-layer encryption technology is adopted to encrypt and store data, secure communication protocols (such as TLS/SSL) are used to encrypt and transmit data, and fine-grained access control mechanisms and intrusion detection and prevention systems (IDS/IPS) are introduced to monitor network traffic in real time and promptly detect and prevent potential attack behaviors. The experimental results show that the national fitness data monitoring system proposed in this study can accurately monitor users' exercise status and health indicators, and provide customized fitness plan recommendations for users based on the POI recommendation algorithm. In terms of network security performance, the system has high efficiency in data encryption and decryption. The access control mechanism can effectively restrict unauthorized users from accessing sensitive data. Moreover, when facing simulated DDoS attacks and SQL injection attacks, the system demonstrates excellent anti-attack and recovery capabilities. Through these tests, the network security performance of the system has been effectively verified.</p>