Research on Health Tourism Recommendation System Based on Behavior Pattern Mining Algorithm and Sensor

This study aims to design a health tourism recommendation system based on behavior pattern mining algorithms and sensor technology. According to the needs of health tourism, select sensor devices suitable for monitoring user physiological data and behavioral characteristics, use sensor technology to collect user physiological data and behavioral characteristics during travel, and establish an individualized behavioral pattern database. Then, combining data mining algorithms to analyze user behavior data, identify user behavior preferences and health needs. Finally, based on personalized behavior patterns and user needs, data mining algorithms are used to analyze user behavior data, identify user behavior preferences and health needs, explore user habits and preferences during travel, and recommend health tourism projects that meet user tastes and health conditions. Research has shown that health tourism recommendation systems have achieved significant results in sensor data collection, behavior pattern mining, and personalized recommendations. After user participation in the experience, there was general feedback that the travel experience was more in line with personal preferences, and the effectiveness of health management was also effectively improved.