Accurate prediction of port traffic flow is a challenging task due to its stochastic uncertainty and time-unsteady characteristics. In order to improve the accuracy of port traffic flow prediction， a port traffic flow prediction model based on knowledge graph and spatio-temporal diffusion graph convolution network， named KG-DGCN-GRU， was proposed， taking into account the external disturbances such as meteorological conditions and the opening and closing status of the port-adjacent highway. The factors related to the port traffic network were represented by the knowledge graph， and the semantic information of various external factors were learned from the port knowledge graph by using the knowledge representation method， and Diffusion Graph Convolutional Network （DGCN） and Gated Recurrent Unit （GRU） were used to effectively extract the spatio-temporal dependency features of the port traffic flow. The experimental results based on the Tianjin Port traffic dataset show that KG-DGCN-GRU can effectively improve the prediction accuracy through knowledge graph and diffusion graph convolutional network， the Root Mean Squared Error （RMSE） is reduced by 4.85% and 7.04% and the Mean Absolute Error （MAE） is reduced by 5.80% and 8.17%， compared with Temporal Graph Convolutional Network （T-GCN） and Diffusion Convolutional Recurrent Neural Network （DCRNN） under single step prediction （15 min）.