Accurate segmentation of mining subsidence areas is crucial for monitoring illegal mining activities, mine remediation, and land use planning. Addressing the segmentation issue of mining subsidence in differential interferometric phase maps, we establish a differential interferometric phase map dataset specific to mining subsidence and propose an improved algorithm EMAGUNet++ based on the UNet architecture. By integrating attention gate and efficient multiscale attention mechanisms, attention weights are focused on relevant local features to enhance segmentation accuracy. Experimental results demonstrate that EMAGUNet++ achieves an intersection over the union index of 0.756 for mining subsidence segmentation, outperforming other classic segmentation models by 1.8% to 9.2%, validating the effectiveness of the model improvements. The improved model accurately segmented mining subsidence persisting over 6 years near Fugu County, and the segmentation polygon achieved an accuracy of 89.3%, showcasing excellent performance.