Abstract: With the deep integration and collaboration between artificial intelligence and weaponry equipment, the intelligent operation and maintenance of radar systems have become an inevitable trend in the development of military equipment. The health management of radar turntables constitutes a critical part of radar intelligent maintenance. In this paper, taking radar turntables as the research object, a multi-source data fusion-based health management framework is established through comprehensive analysis of turntable fault characteristics. And addressing the multi-state parameter characteristics of radar turntable equipment, a two-layer state assessment methodology encompassing data acquisition items and measurement points is proposed, achieving comprehensive assessment of radar turntable health status. The simulation verification result shows that the continuous perception and analysis of state evolution can be realized through real-time monitoring of multi-source data by the health assessment model and data analysis approach of the data-driven radar turntable, which provides decision support for predictive maintenance of radar turntables.