Aiming at the problems of overlapping targets in complex and changeable road scenes， it is difficult to segment image edges and extract small target features. A multi-level attention feature optimization method for real-time semantic segmentation of road scenes is proposed. Firstly， a lightweight residual attention module is designed， taking into account the difference in feature weights at different levels， and optimizing local features of the image through a compressed attention mechanism， thereby improving the edge effect between pixels. Then， the channel attention and depth aggregation pyramid pooling module are designed to further strengthen the extraction of semantic context information， thereby solving the problem of small target information loss. Finally， the attention fusion module is designed to fuse feature information at different scales from top to bottom. It can achieve effective interaction of global feature information and enhance the network’s expression of important features. Experimental tests are carried out on the Cityscapes and CamVid road scene datasets， and the segmentation accuracy is 74.4% and 67.7%， respectively， and the inference speed are 138 frames/s and 148 frames/s. Compared with the excellent methods in recent years， this method improves the loss of image edge information and optimizes the segmentation accuracy of small objects in the image.