Abstract: With the deepening integration and collaboration between artificial intelligence and weaponry equipment, intelligent operation and maintenance of radar systems has become an inevitable trend in the development of military equipment. The health management of radar turntables constitutes a critical component of radar intelligent maintenance. Focusing on radar turntables as the research subject, this study proposes a multi-source data fusion-based health management framework through comprehensive analysis of turntable fault characteristics. Addressing the multi-state parameter characteristics of radar turntable equipment, we establish a two-layer state assessment methodology encompassing data acquisition items and measurement points, achieving comprehensive evaluation of radar turntable health status. Finally, simulation verification of the health state assessment methodology demonstrates that the data-driven radar turntable health evaluation model and data analysis approach enable continuous perception and analysis of state evolution through real-time data monitoring, thereby providing robust decision support for predictive maintenance of radar turntables.