Aiming at the problems of high tracing cost of river crab and consumers' inability to trace monomer river crab information in fine grain, an individual recognition algorithm of river crab carapace image based on migration learning and pyramid convolution is proposed.The algorithm uses pyramid convolution layer to replace ordinary residual convolution block to build a network model, which can extract multi-scale and deep-seated feature information from crab back image. The results show that the accuracy of resnet34 and resnet50 with pyramid convolution structure are 98.38% and 98.51% respectively. Compared with the model with ordinary convolution layer, the accuracy is improved by 5.49% and 1.3%. When the depth of the model reaches 101 layers, the performance of the model is no longer significantly improved.Compared with the new learning model using pyramid convolution structure, the training convergence iteration rounds of the transfer learning method are reduced from 20 rounds to 5 rounds. At this time, the accuracy of the model is 98.88%, which is 0.37% higher than that of the new learning. At the same time, it makes up for the problem of small sample size.This study provides theoretical basis and technical support for individual identification and traceability of river crab.