A recursive radial basis function (RRBF) neural network is applied to construct unsteady aerodynamic model, which leads to a dynamic nonlinear reduced order model (ROM). The widths of basis function in hidden layer is one of important parameters to this aerodynamic model.To investigate the effects of widths for RRBF neural network, mathematical analysis and simulations are executed at first, which shows the relationship between widths and framework of the model during training process. Then cases of NACA 0012 aerofoil with pitching maneuvers are simulated, to test the model under different training maneuvers, previous time steps and flow states. Results show that the widths of basis function have much impact on the stability and generalization ability of this type of aerodynamic model. The best width varies with different training and testing maneuvers. With more samples, higher predicting accuracy of aerodynamic model is guaranteed with widths between 55~75. Predicting results of random pitching maneuver verify the conclusion of best widths scale in the paper.