[Objective] This paper proposes a new drug recommendation algorithm based on the graph neural network integrating patient signs and medication history, aiming to improve the illness diagnosis and treatments. [Methods] First, we constructed a transitive relationship model for abnormal signs and drugs based on the Graph Neural Network(GNN). Then, we designed a precise drug recommendation plan with sign perception and built a heterogeneous graph for the “sign-patient-drug” relationship. Third, our model learned the node representation with sign perception using the R-GCN encoder. Finally, we designed a sign-aware interaction decoder, which integrated the abnormal signs to recommend drugs accurately. [Results] We examined the proposed model with diagnosis and treatment records of three types of diseases from the MIMIC-Ⅲ dataset. Compared with the SVD, NeuMF and NGCF models, the proposed method’s Recall@20 value increased by 5.76, 5.33 and 0.91 percentage point, respectively. Meanwhile, it increased the NDCG@20 value by 5.03, 4.25 and 2.67 percentage point. [Limitations] Our method did not include the dynamic changes of patients’ drug use due to the developments of diseases. [Conclusions] The proposed drug recommendation method is effective and feasible. This model could perceive the impacts of patient signs on medication, which lays foundations for precise drug recommendation algorithm integrating multi-dimensional information.