For the presence of small targets and a large number of long bar-shaped objects in urban streetscape datasets, segmentation is difficult, and although current networks with coding and decoding structures can refine segmentation results, most of them do not make full use of spatial and contextual information, so this paper proposes a semantic segmentation algorithm based on pixel attention feature fusion. Firstly, using ResNet50 as the backbone network, the initial feature fusion is carried out using the null space convolutional pooling pyramid and strip pooling to obtain multi-scale features while circumventing useless information; then the pixel fusion attention module is used to aggregate contextual information and recover spatial information, and finally the attention feature refinement module is used to eliminate redundant information. The algorithm was experimented on the CamVid dataset and the results showed that the algorithm was able to achieve 75.22% mIoU on the validation set and 67.21% on the test set. This is an improvement of 2.51% and 2.86% respectively compared to the DeepLabv3+ network.