Abstract: The rapid identification and precise positioning of the freshwater fish head, belly, and fins are the prerequisites for the robot to realize the rapid grasping of freshwater fish, precise cutting, and the key technology of improving operation efficiency. The freshwater fish body semantic segmentation algorithm for deep learning produces a large number of invalid feature channels in the encoding feature extraction stage, and the continuous down-sampling and pooling operations of the network make certain details of the fish body lost, the network performance is reduced, and the edge segmentation effect is not good of the problems, a semantic segmentation algorithm based on Deeplabv3+ freshwater fish head, belly and fin optimized due to SENet is proposed. Use dilated/atrous convolutions to expand the receptive field, overcome the loss of detailed information, and achieve accurate positioning. At the same time, the optimization of SENet enables Deeplabv3+ to improve the useful features of freshwater fish through learning and suppress the current task of features that are not very useful, the final semantic segmentation mean intersection ratio（MIoU） of each part of freshwater fish reached about 93% on the self-built freshwater fish data set, and the performance was significantly improved and reached the current advanced segmentation level.