Aiming at the problems of low segmentation accuracy caused by the loss of network model information,insufficient context information and poor network generalization ab ility in brain tumor image segmentation,a new brain tumor image segmentation method is proposed.Thi s method is a multi-level connected (MC) brain tumor segmentation model composed of depth gate co nvolution module (DGC) and feature enhancement module (FEM).The depth convolution module is used to reduce t he 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 pool ing is used to reduce the information loss in the down sampling process.The feature weight of the seg mented region is enhanced by the FEM.The experimental results show that t he Dice index of the whole tumor area (WT),tumor core area (TC) and enhanced tumor area (ET) predicted and segm ented brain tumors reaches 0.92,0.84 and 0.83 respectively,and the Hausdorff distance reac hes 0.77,1.50 and 0.92.Compared with many current methods,the segmentation accuracy and calculat ion efficiency of brain tumors are higher,and have good segmentation performance .