Abstract: The middle section of the Tianshan Mountains in Xinjiang is rich in mineral resources, but the problem of land damage has been exacerbated by high-intensity mining activities. In order to solve the problems of low efficiency of mine land damage monitoring and traditional remote sensing interpretation relying on manual experience, this paper proposes an automatic classification model of remote sensing image land damage based on neural network-SENetV2-COT-DeepLabV3, which is based on the DeepLabV3 model and integrates the Contextual Transformer (COT) module and the SENetV2 module, so as to enhance the ability of context feature extraction and channel attention mechanism. Optimize the model's segmentation ability for complex mine features. Firstly, according to the high-resolution series of remote sensing images, a sample set of 59,198 samples was constructed for the middle section of the Tianshan Mountains, which was extended to 177594 samples through data augmentation. Then, the SENetV2-COT-DeepLabV3 model was trained to improve its generalization ability and recognition accuracy, and accurately grasp the distribution and extent of land damage caused by mineral resource development. Finally, through comparative experiments with FCN, UNeT, PSPNeT and other models, it is concluded that the improved model is better than the mainstream models such as FCN and PSPNet in four indicators: MIoU, mRecall, mPrecision, and mDice, and the segmentation accuracy is 1.63%～2.34% higher than that of DeepLabV3. Based on the model, a deep learning remote sensing interpretation system for land damage types in mining areas was built on the Pycharm platform, which has been deployed to the local mine management department, with a recognition accuracy of more than 85%, realizing high-precision and high-efficiency land damage identification, providing an intelligent solution for dynamic monitoring and ecological restoration management of land damage in mining areas, and promoting the coordinated development of mine development and environmental protection.