A finite-time adaptive neural tracking control method is proposed for nonlinear pure-feedback systems with input saturation and output constraints.Finite-time control theoryBarrier Lyapunov functionand Radial Basis Function (RBF) neural network are used to design novel virtual and real input signalswhich solves the finite-time control problem of nonlinear pure-feedback systems with input saturation and output constraintsand ensures that the output of the system can track the reference signal within a finite time and the tracking error of the system is limited to a small neighborhood of the origin within a finite time under the condition of satisfying input saturation and output constraints.Finallya simulation experiment has clarified the effectiveness of the designed controller.