Owing to the high degrees of freedom characteristic of hyper-redundant manipulator (HRM), path planning in complex environments poses significant challenges. To address the unique characteristics of HRMs, this paper proposes a forward propagation-enhanced Rapidly-exploring Random Tree (RRT) configuration planning method. This method divides the planning problem into two phases: the preparatory phase and the obstacle avoidance phase, enabling the computation of the spatial position of each joint throughout the entire planning process. First, an obstacle management method based on circumscribed regular geometric shapes is introduced, which significantly reduces the computational overhead of collision detection for irregular obstacles by dividing obstacle regions into danger zones and warning zones. Second, the preparatory phase and the obstacle avoidance phase are established using the RRT algorithm, constructing the position and configuration of the HRM for each phase. Subsequently, the forward propagation configuration planning method dynamically allocates the arm segment resources of the robotic arm, ensuring a smooth transition between the obstacle avoidance phase and the preparatory phase. Finally, experimental results demonstrate that the proposed method effectively improves the motion efficiency of HRMs in complex environments while ensuring the safety and real-time performance of path planning.