Presently, image inpainting in the inheritance and protection of Chinese traditional embroidery often depend on human labor, with considerable work force and material resources. Furthermore, with the rapid development of deep learning, generative adversarial networks can be applied to repair damaged embroidery relics. An embroidery image restoration method based on improved deep convolutional generative adversarial network (DCGAN) is proposed to solve the above problems. In the generator part, dilated convolution is introduced to expand receptive fields; the addition of the convolution attention-mechanism module enhances the guiding role of significant features in two dimensions of channel and space. In the discriminator part, the number of full connection layers are increased to improve the ability of the network to solve nonlinear problems. In the loss function part, the mean square error loss and confrontation loss are combined to realize embroidery image inpainting through the game process of network training. The experimental results show that the dilated convolution and convolution attention mechanism module improves the network performance and repair effect, and the structural similarity of the repaired image is as high as 0.955. This method enables obtaining a more natural embroidery image-restoration effect, which can provide experts with information such as texture and color as a reference to assist subsequent repair.