To address inefficiency and high collision rates in existing path planning algorithms for mobile robots in complex scenarios, this paper introduces a novel algorithm, TD3-KDHER, which combines Twin Delayed Deep Deterministic Policy Gradient (TD3) with an enhanced version of Hindsight Experience Replay (HER). The core innovation of TD3-KDHER is integrating Kernel Density Estimation (KDE) to utilize the experience buffer for fitting a probability density model, thereby optimizing the selection of pending experiences and new goals. This enhances the likelihood of selecting rare but high-value samples. Additionally, the algorithm incorporates adaptive action noise and action masking strategies to refine the exploration strategy of the agent. Experimental results show that TD3-KDHER outperforms both TD3 and TD3-HER across multiple metrics in various scenarios.