The application of deep learning and self-attention mechanism greatly improves the performance of semantic segmentation network. Aiming at the roughness of the current self-attention mechanism that treats all channels of each pixel as a vector for calculation, we propose a grouped dual attention network based on the spatial dimension and channel dimension. First, divide the feature layer into multiple groups; then, adaptively filter out the invalid basis groups of each feature layer to capture accurate context information; finally, fuse multiple groups of weighted information to obtain stronger context information. The experimental results show that the segmentation performance of this network on the two data sets is better than dual attention network, the segmentation accuracy on the PASCAL VOC2012 verification set is 85.6%, and the segmentation accuracy on the Cityscapes verification set is 71.7%.