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Due to the large amount of information loss generated by the traditional SegNet model during the sampling process, the accuracy of semantic segmentation is low. Therefore, a new encoder-decoder network structure with residual connection is proposed. The multi-residual connection strategy is introduced to retain a large number of detailed information contained in multi-scale images more comprehensively, and reduce the information loss caused by decimation. In order to further accelerate the convergence efficiency of network training and improve the imbalance problem of samples, a cross-entropy loss function with balance factor is designed, and the imbalance phenomenon of positive and negative samples is optimized in a targeted manner, so that the training of the model is more efficient. Experiment shows that this method solves the problems of information loss and inaccurate segmentation in semantic segmentation, and compared with SegNet, the mIoU value of fine labeling on Cityscapes dataset is increased by about 13%.