Abstract: To address the limitations of the surface movement observation stations and InSAR technical means in mining subsidence monitoring, the airborne LiDAR (Light Detection and Ranging, LiDAR) is used to collect 3D point cloud data in the subsidence area and construct a surface subsidence digital elevation model (subsidence DEM) by multi-temporal point cloud to obtain the mobile deformation characteristics of the surface. However, the constructed subsidence DEM contains multiple noise sources that are complex and difficult to remove, which limits the application of this technique in mining subsidence monitoring. An airborne LiDAR point cloud direct comparison algorithm (Cloud to Cloud, C2C) is proposed for mine subsidence monitoring. A working face in Yushen mining area is used as the study area, and the level observation data is used as the reference data, and the subsidence DEM is compared with three mainstream point cloud interpolation algorithms to verify the feasibility and accuracy of the algorithm. The results show that the C2C algorithm can quickly obtain high-precision subsidence values, and its subsidence accuracy is better than the calculation results obtained by the point cloud interpolation algorithm, and the subsidence curve conforms to the general rule of mine subsidence. The algorithm can achieve centimeter-level accuracy, which provides a new reference solution for mine surface movement deformation monitoring and ecological environment restoration.