Fish segmentation in underwater environment is the key technology to realize intelligent measurement such as body length measurement, weight estimation and population counting. In order to improve the accuracy of fish segmentation, an improved Deeplabv3 + fish segmentation method combined with edge supervision is proposed. In the encoder part, fewer down sampling times are used, and convolutional block attention module (CBAM) is added in the shallow layer to reduce information loss and enhance the shallow semantic information; By designing hybrid dilated convolution (HDC) to improve atrous spatial pyramid pooling(ASPP) module, deep features are extracted. In the decoder output part, Canny edge detection operator is combined to introduce edge supervision, and the edge prediction and edge label errors are obtained through the edge loss function to better learn edge features. Finally, the optimized loss function is introduced according to different pixel ratios to further improve the semantic segmentation performance of the model. This method achieves 84. 56% mIoU on VOC2012 dataset, which is 3. 27% higher than Deeplabv3+ method, and verifies its generalization ability. In the ablation experiment on DeepFish dataset, mIoU is as high as 93. 66%, which is higher than common methods such as Deeplabv3+, Unet and PSPNet. This research improves the accuracy of fish segmentation in underwater environment and can provide support for intelligent aquaculture.