The invention relates to a coding and decoding network port image segmentation method fusing semantic flow field, and belongs to the technical field of image segmentation, and the method comprises the following steps:inputting an image to be segmented into a trained coding and decoding network fused with the semantic flow field, and segmenting the port image into a sea type, a land type and a ship type; wherein the coding and decoding network comprises a coding layer, a dilated convolution layer and a decoding layer which are connected in sequence, the coding layer comprises N layers of convolution modules which are connected in sequence, the decoding layer comprises N layers of deconvolution modules which are connected in sequence, each deconvolution module is internally provided with a flow alignment module, and the input of each flow alignment module is in jump connection with the convolution module of the corresponding level in the coding layer. According to the method, the validity of feature information transmission is improved by predicting a semantic flow field between feature maps and monitoring an up-sampling process by utilizing the flow alignment module, and the multi-scale information of the image is acquired by utilizing the cavity convolution layer, so that the method is more suitable for a port image segmentation task, a smooth and complete segmentation result is obtained, and the segmentation precision is relatively high.