Timely monitoring and evaluation of ecological restoration in mining areas is crucial. Based on remote sensing data and deep-learning models, the dynamic changes of bare rock area and vegetation in open-pit mine can be quantitatively monitored and analyzed. Current mining area feature extraction algorithms are limited by single-scale approaches and insufficient information fusion, resulting in low recognition rates. To address this, we proposed an improved Bilateral Segmentation Network (BiSeNetV2) semantic segmentation model (BiSeNetV2 + MSFE + SegHead, BMS), which combines multiscale feature extraction (MSFE) module and segmentation head (SegHead) structures. We utilized BMS model to conduct research on the classification and change monitoring of vegetation areas and mining areas. Our results demonstrated that the accuracy evaluation indicators aAcc, mAcc, and MIoU of the BMS model were better than those of the BiSeNetV2 model, with improvements of 3.5%, 5.5%, and 7.9%, respectively. Meanwhile, compared to the short-term dense concatenate and Twins-PCPVT deep-learning models, the BMS model improved aAcc, mAcc, and MIoU by 3.4%, 8.0%, and 7.3% and 4.4%, 1.1%, and 8.6%, respectively. Accurate and efficient research on ground object classification methods enables quantitative evaluation of mining area environment recovery, providing crucial technical support for ecological monitoring, planning, and governance.