Aiming at the problems of overlapping targets in complex and changeable road scenes, it is difficult to segment image edges, and it is difficult to 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, design the channel The attention and depth aggregation pyramid pooling module further strengthens the extraction of semantic context information, thereby improving 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 to achieve global feature information. Effectively interact and enhance the network"s expression of important features. Experimental tests were carried out on the Cityscapes and CamVid road scene datasets, and the segmentation accuracy was 74.4% and 67.7%, respectively, and the inference speed was 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.