The wide use of deep learning and convolutional neural networks in recent years has been one of the main reasons for performance improvement in image semantic segmentation. However, the current image semantic segmentation algorithms have certain drawbacks. For example, the semantic information is not fully used, and the discrimination between different semantic categories is not large enough. Therefore, we propose a hierarchical context information mechanism to achieve better semantic segmentation performance. The long-range dependency information and local context information (extracted from the hierarchical features) are conducive to enriching information and discriminating among different types of semantic categories. Our experiments demonstrate the effectiveness of the proposed method. The proposed method achieves a segmentation accuracy of 77.2% on Cityscapes val dataset.