High accuracy results of semantic segmentation often rely on rich spatial semantic information and detailed information, but both incurring high computational costs. In order to solve this problem, we propose a real-time semantic segmentation network based on regional self-attention by observing the similarity of local pixels in the image. The network can calculate the regional correlation of feature information and channel attention information through a regional self-attention module and a local interactive channel attention module. Then, it obtains rich attention information with less calculation. The experimental results on the Cityscapes dataset show that the segmentation accuracy and speed of the network are higher than the existing real-time segmentation network.