The work aims to propose a parallel multi-resolution semantic segmentation algorithm integrating feedback attention module, in order to further improve the accuracy of semantic segmentation and solve the problems of low resolution of feature map, random discarding of low-level information features and failure to take into account important contextual information in the current semantic segmentation algorithm. The algorithm exhibited a parallel network structure, which integrated high and low resolution information, retained high-dimensional information as much as possible, reduced the loss of low-level information elements, and improved the segmentation resolution. At the same time, a perceptual attention module with feedback mechanism was embedded in the backbone network to obtain the weight information of each sample from the perspectives of channel, space and global, focusing on strengthening the importance of characteristics among samples. In the training process, the improved loss function was also used to reduce the difficulty of training and optimization. Experiments showed that the proposed algorithm model achieved 77.78% and 58.67% MIOU indexes on Pascal voc2012 and Camvid respectively, and 42.52% on ADE20K, reflecting better segmentation performance. Compared with the previous segmentation network, the algorithm model has a certain degree of improvement. Some modules embedded in other backbone networks still show good performance, which shows that the algorithm model has certain effectiveness and generalization ability.