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Objective Patients with schizophrenia (SZ) suffer from cognitive deficits in working memory, information processing, and selective learning, which are still clinically diagnosed by doctors assessed by scales. In this paper, we propose an auxiliary diagnosis method for schizophrenia based on brain functional connectivity and graph convolution neural network (GCN) without relying on artificial features to realize the automatic classification of schizophrenia. Methods Due to the natural similarity between brain network graphs and graph data, in this paper, we obtained event-related potential (ERP) from a reinforcement learning task with 42 schizophrenia patients and 29 healthy controls (HC), constructed functional connectivity matrices using phase lag indices with electrodes as nodes, and constructed brain network graph data by combining node features, which were inputted into a graph convolutional neural network model for training classification. Results The classification accuracy, precision, F1 score and specificity of SZ and HC when using power spectral density as nodefeatures under the GCN model were 84.21%, 75%, 85.71% and 70%, respectively. The accuracy was improved by 6.43% compared to choosing the original electroencephalogram （EEG） vector as the node feature. The GCN model also improved the accuracy by 3.18% compared to using the random forest classifier. Conclusions In this paper, graph neural network is used to classify EEG signals, and the experimental results show that GCN can effectively recognize SZ patients and realize automatic classification of SZ patients. And the selection of node features under the graph structure has a significant improvement on the classification accuracy relative to the traditional machine learning model, and the effect is better.