Due to the characteristics of electronic medical records （EMRs）， such as the diversity of data types and temporal irregularity inherent， most existing deep learning-based methods cannot simultaneously capture static correlations between different types of clinical data and dynamic temporal dependencies between visits during the feature learning process. To address this issue， this paper proposes a disease prediction model based on multi-domain graph neural network. In this model， a temporal feature learning module that combines code level attention and time aware LSTM is first utilized to obtain the initial feature representation of patient visits. Then， based on the correlation and time interval information between different visits， a visit affinity graph and a visit sequence graph are constructed， and a graph convolutional neural network is used to mine the static and dynamic semantic associations between visit records from these graphs. Finally， a multi-domain feature fusion module based on self-attention mechanism is utilized to combine temporal features and semantic association features to obtain the final patient fusion representation for future disease prediction. The experimental results on two real clinical datasets show that our method outperforms other existing methods and achieves higher prediction accuracy.