Aiming at the problems of low segmentation accuracy caused by the loss of network model information,insufficient context information and poor network generalization ability in brain tumor image segmentation,a new brain tumor image segmentation method is proposed.This method is a multi-level connected (MC) brain tumor segmentation model composed of depth gate convolution module (DGC) and feature enhancement module (FEM).The depth convolution module is used to reduce the information loss of feature information transmitted layer by layer.The control gate unit (CGU) is used to realize the MC of each scale feature map,in which the combination pooling is used to reduce the information loss in the down sampling process.The feature weight of the segmented region is enhanced by the FEM.The experimental results show that the Dice index of the whole tumor area (WT),tumor core area (TC) and enhanced tumor area (ET) predicted and segmented brain tumors reaches 0.92,0.84 and 0.83 respectively,and the Hausdorff distance reaches 0.77,1.50 and 0.92.Compared with many current methods,the segmentation accuracy and calculation efficiency of brain tumors are higher,and have good segmentation performance.