Acquiring surface orthophoto image by UAV technology can quickly and effectively realize the comprehensive monitoring and analysis of mining surface morphology. However, shadows are prevalent in the open-pit mine UAV orthophoto image, which not only interferes with the acquisition of some ground object information, but also reduces the interpretation and recognition accuracy of UAV images. There are few researches on shadow extraction in mining area at present. Existing methods can not meet the needs of shadow identification in open pit mines. And the problems of not establishing shadow data set in opencast mining area, a UAV ortho image shadow dataset is constructed by using manual annotation for the first time. Based on the UNet3+model, a shadow extraction method combining mixed attention mechanism (CBAM) and depth separable convolution layer (DSC) is proposed. By introducing the ResNet feature extractor, feature extraction on five scales is carried out on the original image, and performs full-scale jump connection according to the extracted features to carry out feature fusion. And introduce CBAM attention mechanisms to enhance useful features. The category of each pixel is predicted by the feature map recovered by the deep monitoring mechanism and the decoder. Finally, proposed method is compared with four typical target extraction networks FCN, UNet, UNet++and UNet3. The experimental results show that, compared with Unet3+network, the mPrecision, mRecall, mF1 and mIoU improved by 4.9%, 0.44%, 2.24% and 4.51%, respectively. Proposed method was compared with a variety of existing shadow extraction methods on AISD public data. The experimental results showed that compared with residual supervision network, F1 and IoU improved by 0.27% and 2.62%. It is proved that this method is accurate in shadow extraction, and is suitable for shadow extraction in open pit mining area.