Accurate drug target affinity prediction (DTA) can shorten the drug development cycle, save manpower and material resources, and accelerate the drug development process. Graph Neural Networks (GNN) have been widely used in drug target affinity prediction, but most of the existing methods are based on shallow GNN. Therefore, a graph convolutional network based on the residual structure is proposed. The addition of the residual structure can deepen the network structure, thereby constructing a deep graph convolutional network with 24 graph convolutional layers to capture the characteristics of drug molecules, learn efficient embedding representations, and compare with several state-of-the-art machine learning or deep learning based models on two benchmark drug target affinity datasets. The results show that the proposed model has better predictive performance than other benchmark models, which verifies the effectiveness of the method proposed in this paper.