In order to improve the autonomy of gliding guidance for complex flight missions, a multi-constrained intelligent gliding guidance strategy based on optimal guidance and RL (reinforcement learning) was proposed. Three-dimensional optimal guidance was introduced to meet the terminal latitude, longitude, altitude and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver was proposed, and an analytical terminal velocity prediction method considering maneuvering flight was studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL was studied. This method designed a state space via terminal velocity and an action space with maneuvering amplitude. In addition, it constructed a reward function that integrated the terminal velocity error and gliding guidance tasks, and used Q-Learning to achieve the intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy, and can improve the autonomous decision-making ability under complex tasks effectively.