<p>Consider the sampled-data control problem for a class of high-order systems that possess unknown nonlinear dynamics. Based on the artificial input delay technique, we construct a new output sampling intelligent control scheme and present an <i>adaptive sampled-data output feedback control</i> (ASOFC) algorithm to achieve stabilization of the system. In scenarios where direct state measurements are unavailable, a neural network-based observer is delicately designed to provide state estimates, and the reinforcement learning method is adopted to accomplish the approximation of unknown nonlinear dynamics. Subsequently, we provide a theoretical analysis of the system stability within the Lyapunov-Krasovskii framework. Finally, a numerical example is provided to verify the effectiveness of our proposed approach.</p>