In recent years, with the public paying more and more attention to food safety, the traceability technology has promoted the rapid development in the food industry.In order to study the automatic traceability algorithm of bighead fish, this paper chose the image of bighead fish as the research object to explore the algorithm of automatic traceability of bighead fish, analyzed the characteristics of image data of bighead fish, and proposed two problems to be solved urgently: network model selection, data migration, etc.Therefore, this paper proposes an automatic bighead carp traceability algorithm based on deep learning to solve the existing problems, and the accuracy of the algorithm model can reach 96.865% in the experimental test.In this paper, aiming at the problem of model selection, the mainstream deep learning framework networks at home and abroad are studied. By comparative analysis, the Densenet network is selected. Through the Densenet network model, the eigenvalues learned at each layer of the convolutional neural network can be transmitted back, and the initial effective features of learning can be maintained in the long-term learning process.The efficiency of feature extraction can be improved, and the learning cost can be greatly reduced.To solve the problem of image data migration of bighead fish, this paper adopts the idea of transfer learning and decompositions the training model into two steps: online training and offline training, so as to continuously learn the input characteristics of bighead fish, so as to ensure that high accuracy can still be achieved in the waters with different nutritional types.