This paper selects the typical coal mining subsidence area in Jining city as the research area, and uses the Dynamic Time Warping algorithm to obtain the typical disturbance trajectory characteristics of the region based on Landsat remote sensing images from 1985~2018. The Python decisionmaking classification algorithm was used to cluster and identify the disturbance type and disturbance time of the region, which reveals the quantitative structure and spatial distribution characteristics of the disturbance type and disturbance time of the mining activity. The results show that between 1985 and 2018, the number of pixels in the study area is 48517, and the area is about 43. 67 km 2, accounting for 62. 30% of the total area of the study area. The most disturbed years are mainly concentrated in 1994~2007. The cumulative number of disturbed pixels is 35724, accounting for 73. 78% of all disturbing pixels. In 1998, the number of disturbed pixels was the most during the monitoring period, and the number of pixels was 8868, accounting for 18. 28% of all disturbing pixels. The number of disturbed pixels in the whole research area is gradually decreasing, indicating that the overall disturbance intensity is weakening and the ecological environment is gradually recovering. This study explores a new method for multitemporal remote sensing surface disturbance monitoring. The research results can provide a scientific basis for region ecological environment management.