This article proposes an adaptive finite-time fault-tolerant control scheme based on a smooth event-triggered mechanism for quadrotor trajectory tracking systems subject to actuator faults and external disturbances. The novel finite-time performance function guarantees the tracking errors are convergent within a prescribed time. The smooth event-triggered mechanism is proposed to overcome the discontinuous triggered signals and decrease transmission resource consumption. Meanwhile, the Zone behavior can be excluded by the positive sampling intervals. Then, the radial basis function (RBF) neural networks are utilized to deal with the model uncertainty, and the adaptive laws are designed to estimate the unknown actuator failures effectively. Finally, an adaptive controller based on the novel time-varying barrier Lyapunov function (BLF) is proposed, and rigorous theoretical analyses are conducted to demonstrate that the tracking errors are convergent within the finite time. Simulation experiments verify the effectiveness of the proposed control scheme.