The invention discloses an attribute graph literature clustering method based on a graph convolutional neural network, and belongs to the field of graph data mining. Specifically, literature attribute graph feature learning is carried out by using a cross-layer linked graph convolutional neural network; estimating an optimal cluster number from the node features by using a deep clustering estimation model; alternately executing the two steps to complete training; utilizing the trained model to obtain the characteristics of all to-be-clustered literature attribute graph nodes and the estimated number of clustering clusters; and taking the characteristics and the estimated number of the clustering clusters as input, and obtaining a clustering result of the literature attribute graph by using a k-means clustering method. When a cross-layer linked graph convolutional neural network is trained, a self-separation regularization item based on node pairwise similarity is adopted, so that the characteristics of nodes in the same cluster are similar and the characteristics of nodes in different clusters are far away, and the performance of graph clustering is effectively improved. And the clustering estimation module realizes data-driven clustering cluster number estimation, so that the whole system is more suitable for a real data environment without labels.