Traditional computational fluid dynamics surrogate model cannot effectively simulate the highly nonlinear fluid, and existed deep learning-based surrogate models are difficult to deal with temporal sequence information effectively. Based on the gated recurrent units (GRU) and the multilayer perceptron, a two-dimensional airfoil of a flapping-wing aircraft was studied to establish a model for rapid predict unsteady aerodynamic parameters of the flapping-wing. The real-time prediction for the highly unsteady and nonlinear aerodynamic parameters of the flapping wing was realized. The computational fluid dynamics method was used to obtain the aerodynamic parameters of the flapping two-dimensional airfoil and the parameters were used as samples to train the prediction model. The flapping amplitude, the frequency, the swing angle and the motion time of the flapping wing were fed into the prediction model, and the lift, the drag and the moment in the relevant condition could be quickly output. Experimental results showed that the established prediction model has high accuracy and fast calculation speed. The prediction model could realize real-time high-precision prediction for unsteady aerodynamic parameters of flapping wings.