To effectively solve the problems of intra-class dissimilarity and inter-class similarity, this study proposes a deep learning semantic segmentation model that fuses multiple path features. It utilizes Multipath Fusion Module (MFM) to extract input image features, and dynamically fuses the features extracted from each input path. In the fusion process, the segmentation model dynamically adjusts the fuse on ratio and feature threshold of each path according to the input image, enables highly accurate image segmentation. In the upsampling stage, a guided upsampling strategy helps to avoid edge classification errors due to bilinear interpolation. The proposed network was trained and tested on the Potsdam dataset with good results, with mean intersection over�union (mIoU) of 83.38%, overall accuracy (OA) of 90.21% and an F1 score of 90.86%.