To solve the problems of fuzzy boundary, fracture and target loss in semantic segmentation of long-distance targets in complex environment, a semantic segmentation model using boundary information based on DeepLabV3+ network is proposed.The improved Darknet-53 network is used to replace the original DeepLabV3+ feature extraction network to speed up the models operation, and a feature fusion module is designed as a low-level feature to recover the detailed information in the decoding stage.In order to further optimize the targets boundary, by using the principle of feature sharing, a boundary extraction module is designed to predict the targets boundary by learning multi-scale information through the feature sharing layer of the main network, so as to optimize the segmented image and improve the prediction accuracy of the model at the boundary.The experimental results show that the proposed semantic segmentation model can effectively alleviate the problems of fuzzy boundary, fracture and target loss in the semantic segmentation of long-distance targets.