Abstract: The breakthrough of artificial intelligence provides a new technical approach for the research of aircraft reentry guidance. Aiming at the disadvantage in predictor-corrector guidance, where a large amount of integrations need to be calculated in the prediction step and the bank angle amplitude is iteratively solved based on secant method in the correction step. All the above calculation are difficult to meet real-time demand. Moreover, the dynamic equations need to be integrated both in the longitudinal and lateral guidance, which exist an obvious redundant calculation. In this paper, we propose LSTM (long short term memory)-based intelligent guidance technology. On the one hand, the integration of the dynamic equations in longitudinal guidance is no longer required to predict the range, that is, the prediction step is eliminated, which will greatly reduce the amount of integral calculation and increase the calculation speed. On the other hand, the amplitude of the bank angle will be no longer iteratively solved based on the secant method, that is, the correction step is eliminated. Based on the natural advantages of deep learning both in neural network mapping capabilities and real-time performance, bank angle command is generated by the output of a trained LSTM model based on the real-time state information of the gliding vehicle. At the same time, the longitudinal and lateral guidance periods in the traditional predictor-corrector guidance will be merged into one period, which has the advantage to ensure that the guidance system meets real-time requirements for online using. Monte Carlo simulation analysis show that the proposed method has the advantages both in accuracy and calculation speed under the condition of initial state error and aerodynamic parameter perturbation. The interdisciplinary fusion of guidance technology and artificial intelligence is a hot research direction, which will greatly promote the development of guidance and control of aircraft.