Aiming at the problems that person textual attribute information is not fully utilized and the semantic relationships among the textual attributes are not mined in person re-identification， a person re-identification method based on multi-modal Graph Convolutional neural Network （GCN） was proposed. Firstly， Deep Convolutional Neural Network （DCNN） was used to learn person textual attributes and person image features. Then， with the help of the effective relationship mining ability of GCN， the textual attribute features and image features were treated as the input of GCN， and the semantic information of the textual attribute nodes was transferred through the graph convolution operation， so as to learn the implicit semantic relationship information among the textual attributes and incorporate this semantic information into image features. Finally， the robust person features were output by GCN. The multi-modal person re-identification method achieves the mean Average Precision （mAP） of 87.6% and the Rank-1 accuracy of 95.1% on Market-1501 dataset， and achieves the mAP of 77.3% and the Rank-1 accuracy of 88.4% on DukeMTMC-reID dataset， which verify the effectiveness of the proposed method.