Deep reinforcement learning has shown potential in autonomous driving decision-making. However, vehicle decision-making involves complex information, and limited state information often limits the ability of agents to make optimal decisions. We present a novel on-ramp decision-making method using the SAC (Soft Actor-Critic) algorithm, which integrates the driving intentions of surrounding vehicles. Our model captures the vehicle characteristics of the target lane and its adjacent lanes as the state space. Additionally, we develop a hybrid action space that combines discrete lateral actions with continuous longitudinal actions, enabling the agent to adapt more effectively to intricate driving scenarios. The efficacy of our approach is validated through simulations using SUMO (Simulation of Urban MObility) and real-world road datasets. Comparative analysis of experimental results illustrates that our model surpasses alternative approaches in terms of collision rate and success rate. Moreover, the model exhibits a stable success rate under various road traffic density conditions.