Depth Image Based Rendering (DIBR) is the key technology of virtual view synthesis, but the generated virtual views have large areas of continuous holes. The holes repaired by traditional image repair algorithms lack semantic sense, and the existing partial convolutional neural network distort the edge of the holes area, so this study proposes a partial convolution neural network inpainting algorithm based on edge information. First, the disparity shift is used to generate the virtual view, then the virtual view is assigned and expanded to eliminate the effects of cracks and artifacts on the later holes inpainting, and the edge detector is designed to the partial convolutional neural network which make the network focus on the edge part of the pictures. Finally we use the well learned network model to inpaint large area holes in the virtual view. The experimental results show that the method presented in this paper can repair large areas of continuous holes. The repaired holes area not only has a sense of semantics, but also has finer edge details.