## 1 Introduction  
With the rapid advancement of autonomous driving technology and artificial intelligence, the application of intelligent unmanned ship technology in the shipping industry is becoming increasingly crucial. The autonomous navigation capability of intelligent ships is not only related to the improvement of shipping efficiency but also to maritime traffic safety and environmental protection. Driven by the national strategy of building a \"Smart Yangtze River\", the intelligentization of inland waterway vessels has become key to achieving the sustainable development of the shipping industry. As a core component of intelligent ship autonomous navigation systems, path planning directly impacts the operational efficiency and safety of vessels. Common solutions for path planning include the classic A* algorithm and its various improved forms. For example, [PERSON] et al. [1] addressed the multi-agent path planning problem by incorporating novel optimization algorithms that combine chaotic initialization, reverse search, and differential evolution. [PERSON] et al. [2], while performing well in static environments, show obvious limitations in path optimization and obstacle avoidance in dynamic environments. By combining the improved BITalgorithm with the TD3 deep reinforcement learning method, this paper not only addresses the shortcomings of traditional algorithms in dynamic environments but also introduces hydrodynamic models and multi-threaded parallel computing, further optimizing path smoothness and computational efficiency. Additionally, the reward mechanism designed in this paper comprehensively considers path length, turning cost, and energy consumption, providing new technical approaches for the development of intelligent shipping technology. Compared with the BIT* algorithm proposed by [PERSON] et al. [3], this paper significantly improves the real-time performance and robustness of path planning by introducing TD3's dynamic optimization capabilities. Compared with reinforcement learning-based research by [PERSON] et al. [4] and [PERSON] et al. [5], this paper's hybrid algorithm framework demonstrates superior performance in complex water environments, providing a more efficient solution for autonomous navigation of Unmanned Surface Vehicles. Addressing the shortcomings of existing technologies, this research proposes a deep reinforcement learning-based path planning algorithm for intelligent inland waterway vessels. By integrating an improved BIT* algorithm with a customized reward mechanism, this research develops the BIT*+TD3 hybrid algorithm to adapt to the dynamic environments of inland waterways and improve the efficiency and accuracy of path planning. Simulation results demonstrate that the proposed algorithm exhibits excellent performance in obstacle avoidance success rate, convergence speed, stability, and robustness. The main contribution of this research lies in proposing an innovative path planning method that effectively overcomes the limitations of traditional algorithms in dynamic environments. Through this method, we not only provide a new perspective for the development of inland waterway vessel path planning but also offer strong technical support. Furthermore, this research showcases the immense potential of deep learning technology in enhancing the autonomous navigation capabilities of unmanned ships, laying a solid foundation for the future development of intelligent ship technology. Extensive simulations were conducted to validate the effectiveness and superiority of the proposed BIT*+TD3 hybrid algorithm. The results of these simulations convincingly demonstrate that the algorithm achieves excellent performance in terms of obstacle avoidance success rate, convergence speed, stability, and robustness, especially when compared to traditional path planning methods. The main contribution of this research lies in proposing an innovative path planning method that effectively overcomes the limitations of traditional algorithms in dynamic environments.