Many problems can not be ignored in practical industrial systems, such as input dead-zone and full-state constraints, which have a significant impact on the performance of the system and may even lead to system instability. To overcome the above problems, this paper proposes a fast convergent adaptive neural network event-triggered control strategy for a class of uncertain nonlinear systems. First of all, the barrier Lyapunov function is introduced into the backstepping control framework and the radial basis function neural networks approximate the unknown nonlinear function. At the same time, an adaptive event triggering mechanism is designed to dynamically compensate for the input dead-zone. By reducing the update frequency of the control signal, the communication burden of the system is reduced, and all states of the system do not violate the predefined constraint interval. On this basis, the theory of fast finite-time stability is introduced, which can guarantee the boundedness of all signals in the closed-loop system in finite time and the tracking error quickly converges to the bounded compact set. Finally, the effectiveness of the control strategy is verified by two simulation examples.