The invention discloses a graph convolutional neural network-based drug oral availability and toxicity prediction method. The method comprises the steps of S1, preparing an initial training set; s2, establishing a graph model of drugs, and obtaining a training set; s3, training a graph convolutional neural network and a full-connection neural network by using the training set, and fitting a molecular descriptor of the drug and a mapping relationship between a graph model and oral availability and toxicity of the drug; s4, performing numerical modification on each molecular descriptor feature in the training data, predicting the modified training data by using a neural network, and determining a corresponding predicted value error; s5, sorting all the molecular descriptor features of the medicine, calibrating the molecular descriptor features located in the preorder, deleting the molecular descriptor features of the medicine which are not calibrated, and updating the training data; and S6, retraining the graph convolutional neural network and the full-connection neural network constructed in the step S3. According to the method, the drug oral availability and toxicity prediction model with high prediction precision can be obtained.