Aiming at the quantization of measurement data and random packet loss problems in nonlinear network control systems, this paper presents a data-driven based adaptive iterative learning control algorithm. This algorithm can ensure that the output tracking error can converge to zero after a limited number of iterations, although the system suffers from factors such as data quantification, random packet loss, and uncertainties. Resorting to a pseudo partial derivative based linearization method, the nonlinear system is converted into a linear time-varying system form. Under the framework of linear systems, the adaptive learning gain is updated by the previous batch outputs. Different from the traditional iterative learning control algorithm, the proposed one has no need to predict a priori iteration length and the control system model. Finally, the effectiveness of the proposed algorithm is verified by simulations.