Traditional drug-target association prediction based on biological experiments is difficult to meet the demand of pharmaceutical research because its low efficiency and high cost. In order to solve the problem, a novel Graph Convolution for Drug-Target Interactions (GCDTI) algorithm was proposed. In GCDTI, the graph convolution and auto-encoder technology were combined by using semi-supervised learning to construct an encoding layer for integrating node features and a decoding layer for predicting full-link interactive networks respectively. At the same time, the graph convolution was used to build a latent factor model and effectively utilize the high-dimensional attribute information of drugs and targets for end-to-end learning. In this method, the input characteristic information was able to be combined with the known interaction network without preprocessing, which proved that the graph convolution layer of the model was able to effectively fuse the input data and node characteristics. Compared with other advanced methods, GCDTI has the highest prediction accuracy and average Area Under Receiver Operating Characteristic (ROC) Curve (AUC) (0.924 6±0.004 8), and has strong robustness. Experimental results show that GCDTI with the model architecture of end-to-end learning has the potential to be a reliable predictive method when large amounts of drug and target data need to be predicted.