Abstract: Surface subsidence caused by the exploitation of mineral resources must be considered during the development and utilization of land and space in mining areas. Furthermore, it serves as a significant indication of underground areas subjected to illicit mining. The exploitation of mineral resources is generally conducted in widespread, uneven, and dispersed areas, making it necessary to quickly and accurately identify and extract the spatial distribution of mining subsidence in large areas. This study determined the multitemporal differential interferometric phase diagram of mining areas using the differential interferometric synthetic aperture Radar (D-InSAR) technique. Furthermore, it trained networks for the intelligent identification of mining subsidence by employing deep-learning FCN-8s, PSPNet, Deeplabv3, and U-Net models. The results show that the U-Net model enjoys a high detection accuracy and a short detection time. To improve the semantic segmentation and extraction accuracy of information about mining subsidence, this study introduced the efficient channel attention (ECA) module into the conventional U-Net model during the training. Compared with the conventional model, the improved U-Net model increased the intersection over union (IOU) corresponding to mining subsidence by 2.54 percentage points.