To guarantee the safety control performance of monorail cranes operating in complex track conditions within deep mines, enhancing the accuracy and reliability of dynamic inclination recognition for monorail cranes is necessary. Therefore, this paper proposes a dynamic inclination recognition method of monorail crane based on DFFRLS-AUKF algorithm. Firstly, an adaptive smoothing filtering algorithm is used to filter the acceleration and velocity data collected in real-time to avoid the interference of environmental noise and ensure the integrity of the data. Secondly, the track curvature model is established to achieve the accurate analysis of the entire working conditions of the track, and based on the filtered data, a reliable track curvature value is obtained by combining the dynamic recursive least squares of forgetting factor (DFFRLS) algorithm with the dynamic forgetting factor. Finally, based on the calculated track curvature, the unscented Kalman filter (UKF) is improved by using the Sage-Husa noise estimator, which achieves the self-adaptation of the dynamic Adaptive adjustment of dynamic inclination recognition, and the accuracy of emotional inclination recognition is improved. Experiments show that the proposed DFFRLS-AUKF algorithm improves the dynamic inclination recognition accuracy by 25.25% and 39.5% on average compared with the traditional algorithm during the testing of monorail crane in monorail section 1 and monorail section 2, which demonstrates that the DFFRLS-AUKF algorithm has good accuracy and reliability under different track conditions, and effectively guarantees the safety of monorail crane driving under complex track conditions.