In order to improve the intelligent level of river water level and flow data assimilation, a data assimilation method of Saint-Venant equations was proposed based on physics-informed neural networks (PINNs). The dual-output network structure was used to solve the assimilation problem of the dual-output equations and the simulated measured data were used as the boundary conditions and initial conditions. The ablation experiments were carried out to verify the effectiveness of the assimilation model by adding the spatio-temporal mapping scaling and the balance weight coefficient in the network, and the robustness of the proposed assimilation method in the absence of some measured values. The results show that the data assimilation results of the one-dimensional unsteady Saint-Venant equations are consistent with the results of the Preissmann four point implicit difference method, and the assimilation accuracy is also steadily improved with the increase of the number of assimilation sections. The data assimilation method of Saint-Venant equations based on PINNs is effective and has strong adaptability to unsteady flow simulation.