The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques. First, we build a single artistic comment graph based on co-occurrence relations and document word relations and then train an art graph convolutional network (ArtGCN) on the entire corpus. The nodes, which include the words and documents in the topological graph are initialized using a one-hot representation; then, the embeddings are learned jointly for both words and documents, supervised by the known-class training labels of the paintings. Through extensive experiments on different classification tasks using different input sources, we demonstrate that the proposed methods achieve state-of-art performance. In addition, ArtGCN can learn word and painting embeddings, and we find that they have a major role in describing the labels and retrieval paintings, respectively.