In view of the reduction of segmentation accuracy caused by different scales of focus area,fuzzy boundary and uneven intensity of surrounding tissues in medical images, a brain tumor image segmentation model based on double decoder is proposed.In order to enhance the representation of features,a high-order differential residual module is proposed,and the expanded convolution with different void rates is used to extract the feature coding,which improves the segmentation performance of the network model;The context semantic information perception module is introduced to extract more fine information from different target scales,which improves the ability to capture structural details and reduces the feature differences between codecs.The selective spatial aggregation attention module (SAAM) is used in the spatial decoding path to increase the weight proportion of effective spatial features and reduce the interference of invalid features. Experimental verification on different brain tumor data sets is completed. The experimental results show that the Dice coefficient,average intersection union ratio,sensitivity,specificity and accuracy of the proposed algorithm are 93.35%,90.71%,91.15%,99.94% and 96.75%,respectively.