Considering the hypersonic aerospace vehicle, with high dynamic, strong varying parameters, strong nonlinear, strong coupling, and the complicated flight environment, conventional flight control methods based on linear system may become invalid. To the high precision and reliable control problem of this vehicle, nonlinear flight control strategy based on neural network robust adaptive dynamic inversion is proposed. Firstly, considering the nonlinear characteristics of aerodynamic coefficients varying with Mach numbers, attack angle, and sideslip angle, the complete nonlinear 6-DOF model of RBV is established. Secondly, based on the time-scale separation, using the nonlinear dynamic inversion control strategy achieves the pseudolinear decoupling of RBV. And then, using the neural network with single hidden layer approximates the dynamic inversion error for system model uncertainty. Next, the external disturbance and network approximating error are suppressed by robust adaptive control. Finally, using Lyapunov’s theory proves that all error signals of closed loop system are uniformly bounded finally under this control strategy. Nonlinear simulation verifies the feasibility and validity of this control strategy to the RBV control system.