To address the aerodynamic load reduction requirement when the launch vehicle flying in high wind zone during the ascending phase, an intelligent attitude control method with adaptive learning rate was proposed. Taking a certain type of launch vehicle as the research object, the dynamic model in the pitch plane was established. A deep reinforcement learning framework suitable for flight control of the launch vehicle during the ascending phase was developed based on soft actor-critic, and a reward function that comprehensively considers attitude tacking accuracy and stability, and load reduction effectiveness was designed. On this basis, an adaptive iteration of learning rate was implemented based on a step-size learning rate scheduler to quickly improve the convergence velocity and find the optimal solution of the controller. Besides, an early stopping mechanism which can automatically end the training process was designed to enhance the training efficiency. Simulations show that the proposed method can effectively achieve load reduction of the launch vehicle while ensuring attitude tracking accuracy and stability. Additionally, it has strong robustness and adaptability to random wind disturbance.