In order to solve the problem that there are many and irregular bone fragments in the focal area of the distal radius fracture, which causes the doctor’s missed diagnosis and high rate of misdiagnosis, this paper uses the clinical cases of distal radius fracture collected by the research group to propose a supervised automatic distal radius fracture deep learning model. The experiment also introduces the concept of migration learning, which improves the training efficiency of the diagnostic model. Finally, the experiment uses a cross-validation method to evaluate the model. The results show that the classification results of the proposed diagnostic model are better than traditional machine learning and classic deep learning classification models. The classification accuracy rate reaches 84.2%, which is 4% higher than the classic deep learning model. The network structure is simple, the calculation speed is fast, with certain robustness and strong generalization ability.