To address the problem of MRI brain tumor region misidentification and spatial information loss of segmentation network,an MRI brain tumor image segmentation method based on dual-branch feature fusion is proposed.First,the contextual information of the network is extracted by structurally the re-parameterization visual geometry group and attention model (RVAM) in the primary branch,and then the rich spatial information is obtained in the secondary branch using deformable convolution and pyramid pooling model (DCPM),after which the feature fusion module is used to fuse the feature information of the two branches.Finally,the attention model is introduced to strengthen the weight of segmented targets in the up-sampling process at decoding.The proposed method has been experimentally validated on the Kaggle_3m and BraTS2019 datasets,and the experimental results show that our method has good brain tumor segmentation performance,where the Dice similarity coefficient and Jaccard coefficient reach 91.45% and 85.19% on Kaggle_3m,respectively.