With the increasing severity of global climate change and energy efficiency issues, the management of building thermal comfort has become a research focus. However, existing building thermal comfort systems often fail to provide personalized adjustments based on the needs of different users and generally suffer from low energy efficiency. Although some systems based on sensors and intelligent regulation have been developed to address these challenges, most existing systems rely on a single temperature control scheme and fail to effectively achieve personalized adjustments. Furthermore, the effectiveness of these systems in multi-user environments has not been fully validated. This study proposes a multi-user personalized building thermal comfort interaction management system. The system collects real-time environmental data and user feedback, utilizing a weighted average algorithm to optimize the adjustment of temperature, humidity, and airflow, aiming to balance the personalized needs of different users with building energy efficiency. The system design incorporates a sensor network and intelligent analysis, providing a flexible user interface and dynamic adjustment strategies. It is capable of simultaneously meeting the thermal comfort needs of multiple users within the same space, while effectively improving building energy efficiency and comfort experience.