Road scene semantic segmentation is a crucial task in autonomous driving environment perception. In recent years, Transformer neural networks have been applied in the field of computer vision and have shown excellent performance. Addressing issues such as low semantic segmentation accuracy in complex scene images and insufficient recognition capabilities for small objects, this paper proposes a road scene semantic segmentation algorithm based on Swin Transformer with multiscale feature fusion. The network adopts an encoder-decoder structure, where the encoder utilizes an improved Swin Transformer feature extractor for road scene image feature extraction. The decoder consists of an attention fusion module and a feature pyramid network, effectively integrating semantic features at multiple scales. Validation tests on the Cityscapes urban road scene dataset show that, compared to various existing semantic segmentation algorithms, our approach demonstrates significant improvement in segmentation accuracy.