Most semantic segmentation networks use bilinear interpolation to restore the resolution of the high-level feature map to the same resolution as the low-level feature map and then perform fusion operation， which causes that part of high-level semantic information cannot be spatially aligned with the low-level feature map， resulting in the loss of semantic information. To solve the problem， based on the improvement of Bilateral Segmentation Network （BiSeNet）， a High-Low dimensional Feature Guided real-time semantic segmentation Network （HLFGNet） was proposed. First， High-Low dimensional Feature Guided Module （HLFGM） was proposed to guide the displacement of high-level semantic information during the upsampling process through the spatial position information of the low-level feature map. At the same time， the strong feature representations were obtained by the high-level feature maps， and by combining with the attention mechanism， the redundant edge detail information in the low-level feature map was eliminated and the pixel misclassification was reduced. Then， the improved Pyramid Pooling Guided Module （PPGM） was introduced to obtain global contextual information and strengthen the effective fusion of local contextual information at different scales. Experimental results on Cityscapes validation set and CamVid test set show that HLFGNet has the mean Intersection over Union （mIoU） of 76.67% and 70.90% respectively， the frames per second reached 75.0 and 96.2 respectively. In comparison with BiSeNet， HLFGNet has the mIoU increased by 1.76 and 3.40 percentage points respectively. It can be seen that HLFGNet can accurately identify the scene information and meet the real-time requirements.