In order to suppress the background interference in the complex environment of the openpit mine and perform accurate semantic segmentation of the safety retaining wall, a segmentation method of the safety retaining wall of the openpit mine based on the improved DeepLabV3+ network is proposed. First, the backbone network adopts the lightweight MobileNetV2 network, which effectively reduces the amount of network parameters and computation through the depthwise separable convolution and inverted residual structure. Then, a hybrid attention module is added for channel and spatial feature enhancement, which can avoid the loss of edge information. Finally, data augmentation and transfer learning are used to solve problems with fewer target datasets and improve the generalization ability of the model. The experimental results show that the method has a good segmentation effect, with MIOU and MPA of 8506% and 9294%, respectively, which are better than the original network and other classic network models.