In order to solve the problem that it is difficult to conduct dynamic modeling for cross-domain interceptors flying in wide-speed and large-space domains and there are no relevant models，a data-driven online reinforcement learning attitude control method is proposed.First，inspired by the zero-sum game，the interference is considered as a part of the system input to design the performance index function.The purpose of the actual interceptor control quantity input is to minimize the performance index function and improve the system performance，while the effect of interference is opposite.Then，the corresponding approximate solution is obtained through online learning by constructing a critic network，and the uncertainty is handled by updating the weights dynamically.Different from the traditional model-based online reinforcement learning solution method，the data-driven reinforcement learning （RL data driven）method no longer requires the dynamic model information of the interceptor system，but only uses the input and output data of the system to drive the network online learning and updating of weights.Finally，the effectiveness of the proposed method is verified by simulation.