This paper is concerned with establishing an adaptive fixed-time control strategy for nonstrict-feedback nonlinear systems with event-triggered mechanism, which makes sure that the upper bound of the convergence time can be known in advance and is independent of the initial states. Firstly, the piecewise functions and the command filtered technique are separately utilized to avoid the emergence of singularity problem and computational complexity problem. Secondly, neural networks are not only exploited to approximate unknown nonlinear functions and further deal with nonstrict-feedback structures but also assisted in achieving fixed-time stability with the application of command filter. Then, the construction of event-triggered control signal by applying relative threshold strategy is effectual means of saving communication resources, and the design of an estimator that identifies the adaptive parameter can effectively dodge the occurrence of over-parametrization. Furthermore, the proposed control algorithm enables that: (1) the tracking error converges near the origin in a fixed time; (2) all signals of the closed-loop system remain bounded; (3) Zeno behavior will not occur. Finally, the validity of the proposed control strategy is verified via a practical example of the electromechanical system and a comparative simulation.