In this paper, by employing a recurrent neural network and a critic neural network (CNN), we design an improved dynamic event-triggered controller for a class of non-affine continuous-time nonlinear systems. To address the transformation of the robust-optimal control problem, an additional utility function reflecting the disturbance is introduced. Besides, a system identifier is utilized for reconstructing the non-affine dynamics to generate an affine model. For reducing the waste of communication resources, a dynamic event-triggered control strategy is developed to replace the traditional time-based structure and improve static event-triggered control design. In addition, we develop an enhanced CNN weight updating law, which allows for greater flexibility in the process of weight selection compared to the conventional approach. The dynamic event-triggered controller is designed by using the CNN framework. Finally, a simulation of a modified torsional pendulum system is performed to demonstrate the effectiveness of the constructed method.