In view of the incompleteness of the clinical diagnosis method of liver fibrosis and the incompleteness of the feature extraction of traditional machine learning methods, by the deep transfer learning method, this study uses the pre-trained ResNet-18 and VGGNet-11 models for the diagnosis of liver fibrosis. Different degrees of transfer training were performed using the rat liver fibrosis nuclear magnetic resonance image dataset provided by Southern Medical University. The two models were trained using six network migration configurations on the MRI image datasets collected by four different parameters. The experimental results show that the use of T1RHO-FA parameters to acquire nuclear magnetic resonance images and the use of VGGNet-11 model can improve the accuracy of liver fibrosis staging diagnosis. At the same time, compared with the ResNet-18 model, the deep model migration learning method can stably improve the accuracy and training speed of the VGGNet-11 model for liver fibrosis staging diagnosis.