To address the issues of local feature loss and low extraction accuracy faced by deep neural networks in remote sensing image road extraction， a multi-scale context-aware network was proposed based on the SwinUnet network for remote sensing image road extraction. Firstly， a branch with a context aggregation module was designed in the encoder to enhance the extraction of contextual information and alleviate the problem of semantic ambiguity caused by occlusion. Secondly， to solve the problem of semantic information mismatch between the encoder and decoder and to improve the model's ability to extract spatial information， a spatial feature extraction module was introduced in the skip connections， replacing the direct copying of encoder features in SwinUnet. Finally， a feature compression module was designed in the down-sampling stage to reduce information loss in the encoder and enhance the network's segmentation capability. The test results on the Massachusetts road dataset show that this method achieved F1， IoU， Pr， and Re scores of 80.91%， 69.40%， 78.03%， and 65.20%， respectively. In comparison with mainstream methods such as UNet and SwinUnet， the IoU improved by 4.45% and 2.72%， respectively， demonstrating that the proposed algorithm effectively improves the accuracy and performance of remote sensing image road extraction through global modeling， context enhancement， and information matching optimization.