The automatic reconstruction of image and document from their fragments is a classic and challenging problem in image processing. It has very important theoretical significance and application value in criminal investigation, archive restoration and more. This paper introduces a novel method for the reconstruction of shredded images by combining image processing techniques with graph reconstruction strategies. First, the stitching of fragments is treated as a graph reconstruction problem. Each chip of the image is considered a vertex within a graph. Second, the deformable convolutional networks are employed to extract features in the boundary area of the fragment. Third, we propose a conditional graph generative adversarial network to generate the adjacency matrix and reconstruct the graph topology. In this process, the gradient-weighted class activation mapping is applied to capture and visualize the adjacent boundaries of fragments. By calculating the center of mass and gradient direction within the activated area, we can accurately align fragments. Finally, the matched fragments are iteratively stitched together on the basis of the reconstructed graph and adjacent boundaries. The experimental results show that compared with other algorithms, the proposed method exhibits higher precision and reliability across multiple evaluation indexes.