In order to solve the problems that the low-level features of the backbone are not fully utilized， and the effective features are lost due to large-times upsampling in DeepLabV3+ semantic segmentation， a Cumulative Distribution Channel Attention DeepLabV3+ （CDCA-DLV3+） model was proposed. Firstly， a Cumulative Distribution Channel Attention （CDCA） was proposed based on the cumulative distribution function and channel attention. Then， the cumulative distribution channel attention was used to obtain the effective low-level features of the backbone part. Finally， the Feature Pyramid Network （FPN） was adopted for feature fusion and gradual upsampling to avoid the feature loss caused by large-times upsampling. On validation set Pascal Visual Object Classes （VOC）2012 and dataset Cityscapes， the mean Intersection over Union （mIoU） of CDCA-DLV3+ model was 80.09% and 80.11% respectively， which was 1.24 percentage points and 1.02 percentage points higher than that of DeepLabV3+ model. Experimental results show that the proposed model has more accurate segmentation results.