To address the limitations of traditional lane-keeping methods that rely on high-fidelity models and significant computational burden, this paper proposes a lane-keeping assist strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed system integrates vehicle dynamics models with a Deep Reinforcement Learning (DRL) framework. Furthermore, a specialized reward function considering both task completion and lateral error is designed to optimize training convergence speed and tracking accuracy, thereby achieving more precise and efficient performance across various curve scenarios. Simulation experiments demonstrate that the proposed strategy yields significantly smaller lateral errors compared to the traditional Model Predictive Control (MPC) method. Specifically, the Mean Absolute Lateral Error (MALE) is reduced by 42%~72% compared to MPC, highlighting the potential of DRL in lane-keeping tasks.