Most existing deep clustering methods are employed to minimize the reconstruction loss. However, the identification ability of potential representation is not necessarily related to the reconstruction loss. Moreover, these deep clustering methods focus only on extracting useful features from the sample itself and seldom consider the structure information behind the sample. To resolve these problems, a new structured deep discriminant embedded coding network clustering (SDDECC) algorithm is proposed for unsupervised image clustering. First, the maximum mutual information and minimum prior distribution constraints are embedded in a multilayer convolutional autoencoder network. Then, the feature representation learned by the deep discriminant error correction network (DDECN) module is integrated into a graph convolutional neural network (GCN) module by the transfer operator. Finally, Kullback-Leibler (K-L) divergence is used in combination with the potential feature distribution generated by the dual network structure and is trained end-to-end to guide the clustering learning. The experimental results show that SDDECC algorithm can effectively extract more discriminative deep features than those obtained using traditional methods. Moreover, because the attribute information of the sample itself and the structural information between the samples are integrated in the GCN, the model shows good clustering.