<p>Spin, a highly complex and hazardous flight condition, poses a significant threat to pilot and aircraft safety. Modern high-performance aircraft, designed for exceptional maneuverability, are particularly susceptible to loss of control and subsequent spin entry. This study investigates a novel spin recovery strategy based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning to overcome the limitations of traditional methods in addressing the inherent nonlinearity and uncertainty of spin dynamics. First, using a high-fidelity aircraft dynamic and kinematic model, the spin recovery problem was formulated as a Markov Decision Process (MDP). The DDPG algorithm is then employed to train an agent to learn an optimal control policy through interaction with the simulated flight environment. Simulation results demonstrate that the DDPG-based control law achieves significantly faster and smoother recovery trajectories compared to conventional dynamic inversion control. Furthermore, the proposed approach exhibits robust performance in the presence of control surface and engine failures. These findings suggest that DDPG-based control offers a promising and robust solution for advanced spin recovery systems, contributing to enhanced flight safety and the mitigation of aircraft loss-of-control accidents.</p>