Achieving seal segmentation precisely， it is benefit to intelligent application of the Republican archives. Concerning the problems of serious printing invasion and excessive noise， a network for seal segmentation was proposed， namely U-Net for Seal （UNet-S）. Based on the encoder-decoder framework and skip connections of U-Net， this proposed network was improved from three aspects. Firstly， multi-scale residual module was employed to replace the original convolution layer of U-Net. In this way， the problems such as network degradation and gradient explosion were avoided， while multi-scale features were extracted effectively by UNet-S. Next improvement was using Depthwise Separable Convolution （DSConv） to replace the ordinary convolution in the multi-scale residual module， thereby greatly reducing the number of network parameters. Thirdly， Binary Cross Entropy Dice Loss （BCEDiceLoss） was used and weight factors were determined by experimental results to solve the data imbalance problem of archives of the Republic of China. Experimental results show that compared with U-Net， DeepLab v2 and other networks， the Dice Similarity Coefficient （DSC）， mean Intersection over Union （mIoU） and Mean Pixel Accuracy （MPA） of UNet-S have achieved the best results， which have increased by 17.38%， 32.68% and 0.6% at most， and the number of parameters have decreased by 76.64% at most. It can be seen that UNet-S has good segmentation effect in the dataset of Republican archives.