Aiming at the differences in tumor status presented by different modalities of MR brain tumor images and the limitations of feature extraction by convolutional neural networks (CNNs),a method of brain tumor image segmentation based on multimodal fusion is proposed.The segmentation model is based on the U-net network,which innovate a multimodal image fusion approach to enhance the feature extraction capability,while a channel cross transformer (CCT) module is introduced instead of the jump connection structure in the U-net to further the deep and shallow feature disparity and spatial dependency,fusing the multi-scale features effectively and enhancing the tumor segmentation capability.The results of multi-objective segmentation are verified on the BraTS dataset.Quantitative analysis and comparison of frontier network segmentation results shows that the proposed method has good segmentation performance.The Dice coefficients of three tumor regions are 80%,74% and 71% respectively.