ing at the problem of large computation and complicated process of segmentation for whole tumor lesion in segmented magnetic resonance imaging (MRI) three-dimensional images, a fully automatic segmentation algorithm based on deep learning is proposed. A dual pathway three-dimensional convolutional neural network model is constructed on the dilated convolution path filled with jagged holes to extract multi-scale image blocks for training and capture large-scale spatial information. The shallow features are superimposed to the end of the network by using the identity mapping feature of dense connection. The swollen area, enhanced area, core area, and cystic area are segmented in the multi-modal MRI image. The model is segmented and tested in the BraTS 2018 dataset. The results show that the average Dice coefficients of the whole tumor area, core area and enhanced tumor area segmented by the model are about 0.90, 0.73 and 0.71, respectively, which is equal to the performance of the current algorithms and has a high degree of automation integration.