Path planning and its optimization is a critical and difficult task for a mobile robot in a complex and unknown environment. To tackle this problem, we propose an improved SAC (HM-SAC-CA) algorithm for path planning in unknown complex environments. First, based on the SAC maximum entropy framework, a deep reinforcement learning algorithm with clipped automatic entropy adjustment is proposed to improve the quality of policy learning by suppressing entropy evaluation. Second, an innovative hierarchical experience storage structure is constructed during experience replay, and the overfitting phenomenon caused by using good experiences is eliminated by a bias-free sampling strategy. Finally, a posture reward function and a staged incentive mechanism are proposed. The staged incentive mechanism uses both the sparse reward function and the posture reward function in stages to reduce the blindness of exploration during training and accelerate the training learning process. Experiments are conducted using a simulated Turtlebot3 and a real mobile robot and the results validate the performance of the proposed work.