The rapid advancement of automation technology and robotics requires more precision in mobile robot path planning. To address the problems of poor convergence stability, low sample efficiency, and insufficient environmental adaptability in deep reinforcement learning for path planning in complex environments, this paper proposes an enhanced path planning algorithm based on dueling double deep Q-network (R-D3QN). By constructing a dual-network architecture to decouple the action selection and value estimation processes, this method effectively alleviates the Q-value overestimation problem, thereby improving convergence stability. In addition, this method designs a temporal-prioritized experience replay mechanism combined with the spatiotemporal feature extraction capabilities of long short-term memory (LSTM) networks to improve sample utilization efficiency. Finally, this method proposes a multi-stage exploration strategy based on simulated annealing to balance exploration and exploitation, thereby enhancing environmental adaptability. Experimental results demonstrate that, compared to the traditional DQN algorithm, the R-D3QN algorithm achieves a 9.25% increase in average reward value, a 24.39% reduction in convergence iterations, and a 41.20% decrease in collision frequency in simple environments. In complex environments, it shows a 12.98% increase in average reward value, an 11.86% reduction in convergence iterations, and a 42.14% decrease in collision frequency. Furthermore, the effectiveness of the proposed algorithm is validated when compared with other enhanced DQN algorithms.