In order to improve the accuracy of magnetic resonance image segmentation, a magnetic resonance image segmentation method based on residual network and wavelet transformation is proposed. First of all, the discrete wavelet transformation is adopted to fuse different sequences of magnetic resonance images, it leads the fusion image contains more texture information and structure information; then, a residual network including channel attention model and spatial attention model is proposed, thus the network can focus on the target region, and the residual block is included to reduce the vanishing gradient problem of deep neural networks. Finally, validation experiments are carried on the public Brain Tumor Segmentation Challenge 2015 dataset, the results show that the proposed method achieves good effect of average Dice score for whole tumor area, core tumor region and enhanced tumor region.