Abstract: Monorail cranes have an irreplaceable role in the auxiliary transport system in complex deep mine environment, which directly affects the transport safety performance and efficient energy utilization due to the current inability to effectively and accurately identify the load quality and track slope of monorail cranes. For this reason, this paper proposes a method for identifying key parameters of safety performance of unmanned monorail cranes in mines based on DFFRLS-AEKF. Based on the structural characteristics of the monorail crane and the characteristics of track transportation, a longitudinal dynamics model is established for the load mass and track slope with strong coupling relationship; the parameters of the longitudinal dynamics model are identified online in real time based on the operational data and the recursive least squares algorithm with dynamic forgetting factor to achieve the accurate decoupling of the load mass and track slope; and based on the decoupled longitudinal dynamics model and the identified model parameters, the current load mass identification is dynamically modified. Based on the decoupled longitudinal dynamics model and the identified model parameters, the current load quality recognition value is dynamically corrected to eliminate the error and complete the high-precision recognition of load quality; from the identified longitudinal dynamics model parameters and operation data, the Sage-Husa adaptive extended Kalman filter algorithm is applied to dynamically update the system noise covariance and error covariance, filter out the environmental noise interference, adjust and correct the current track slope value in real time, and ensure the accuracy of track slope recognition. Accuracy. The simulation and practical application show that the error between the load mass recognition value and the actual value is within 3.2% and the error between the running track slope recognition value and the actual value is within 5.3% under various working conditions. The method achieves real-time and accurate acquisition of key parameters of the safety performance of the unmanned monorail crane, effectively reduces the occurrence of safety accidents of the unmanned monorail crane, and significantly improves the efficient utilization of energy of the unmanned monorail crane.