<p>In this paper, a finite-time adaptive neural networks relative event-triggered command-filtered tracking control for the stochastic nonlinear systems with the signals constraint is presented. Firstly, the unknown nonlinear functions are approximated using neural networks radial basis function, and the barrier Lyapunov function is utilized to ensure the output signal constraint within a predefined range. Secondly, a finite-time command-filtered approach is adopted in the controller design to achieve finite-time stability. Thirdly, a relative threshold event-triggered mechanism is introduced to reduce the communication costs, and the threshold parameters are dynamically adjusted in response to the actual tracking performance, thereby enhancing the adaptability and efficiency of the control strategy. Finally, simulation results demonstrate the effectiveness of the proposed control method.</p>