The dynamic response of interwell injection-production in injection and production wells is an important parameter in the process of reservoir development. The correct evaluation of the dynamic response of interwell injection-production can provide a theoretical basis for optimizing process measures of flow field control in the later stage of reservoir development. Injection-production well pattern can be equivalent to a graph structure，and there is a strong correlation between well points. Therefore，the dynamic response of interwell injection-production is studied based on the graph neural network.The fluid production of producing wells is predicted，and backpropagation learning is carried out，so as to quantitatively characterize the dynamic response relationship of interwell injection-production at different times based on the graph attention network，the variation amount of water injection per unit time in injection wells，the variation amount of liquid production per unit time in producing wells at multiple time nodes，and the parameters such as bottom hole pressure and well location data in the information of physical flow process. The results show that the new method is suitable for the actual reservoir with a large number of wells and frequent well opening and shutting operations. The method has the advantages of low cost and has combined dynamic and static parameters，so it can be widely applied.