In order to solve the misdiagnosis of encephalitis and glioma in clinical diagnosis while using MRI images, we proposed a classification method of convolutional layer feature transfer combined with active sample labeling. The method firstly adopts the convolutional layer features parameter transfer and uses the multi-modal MRI image data for the fine-tuning of models to verify the distinguishing ability of different MRI modal features. Secondly, in view of the difficulty of sample labeling, an entropy uncertainty based active labeling algorithm is designed to extract the uncertainty information of samples to further improve the convergence speed and generalization ability of the model. Experiments were carried out on a dataset of 175 cases (118 cases of encephalitis and 57 cases of glioma) collected by the radiology department of the First Affiliated Hospital of Chongqing Medical University. The results show that the classification accuracy under cross-validation reached 95. 08% and area under the curve reached 0. 98. The accuracy of the model was superior to the method mainly relying on the experience of doctors at present; and the accuracy and area under the curve was 17. 51% and 0. 15 higher than that of doctors, respectively. At the same time, only 30% of the data samples need to be annotated, so the model can achieve optimal performance, reduce a lot of data annotation work, and provide meaningful guidance for the initial diagnosis.