Abstract: To improve the accuracy of individual identification of Sousa Chinensis, a fine-grained identification model based on the loss of inter-class difference is proposed. Firstly, based on the fact that the features of different types of images are different, a loss function of inter-class difference is designed; Secondly, the channel number of several convolution layers about VGG16 is adjusted according to the number of categories. And two full con-nection modes are designed for identification of large-scale and small-scale individuals respectively; Finally, the proposed loss and the existing loss are combined to stimulate the network to learn more distinguishing features. 2177 images of Sousa Chinensis taken in Xiamen Bay are selected and manually labeled as 30 individuals. Ex-periments on this dataset show that the proposed loss function can improve the accuracy rate by 1.05% to 98.65%, which is at least 0.9% higher than the mainstream fine-grained identification algorithms.