Abstract: The survey of hidden disaster causing factors in coal mines is evolving from the traditional static reporting mode to a dynamic intelligent diagnostic system. Although existing static reports provide basic data, they suffer from issues such as poor timeliness, insufficient information fusion, and reliance on manual judgment. This paper takes the survey project of hidden disaster causing factors in Jiudaoling Coal Industry in Liaoning Province as an example, analyzes its technical methods and limitations, and proposes a development path for building an integrated system of “real-time monitoring, data fusion, intelligent diagnosis, and dynamic prevention and control”. The system integrates multi-source real-time monitoring data and artificial intelligence algorithms to achieve dynamic identification, risk prediction, and intelligent prevention and control of disaster causing factors, thereby enhancing the intrinsic safety level and digital governance capability of mines.