In view of the problems of deep network depth and lack of context information in medical image segmentation, which leads to the reduction of segmentation accuracy, an improved U-Net-based magnetic resonance imaging (MRI) brain tumor image segmentation algorithm is proposed in this paper. The algorithm forms a deep supervised network model by nesting residual block and dense skip connections. Change the skip connection in U-Net to multiple types of dense skip connection to reduce the semantic gap between the encoding path and the decoding path feature map; add a residual block to solve the degradation problem caused by too deep network to prevent the network gradient from disappearing. Experimental results show that the Dice coefficients of the algorithm for segmenting the whole tumor, tumor core, and enhanced tumor are 0.88, 0.84, and 0.80, respectively, which meets the needs of clinical applications.