Abstract: In response to the requirements of rebar plane stability on the tying sequence during rebar-tying operations, this paper proposes an order-constrained coverage path planning method based on a priority-aware neural network fused with the A* algorithm.First, a grid map of the rebar plane is constructed according to the structural characteristics of both the rebar surface and the rebar-tying robot. Then, considering the sequential constraints inherent in the rebar-tying process, as well as obstacle avoidance and the robot’s kinematic feasibility, a target-reconstructed priority-aware neural network is developed to generate feasible paths. Subsequently, an A*-based dynamic path correction mechanism is integrated to address dead-zone escape problems that may occur during planning. Finally, to quantitatively evaluate the stability of the rebar plane after tying, a spatio-temporal stability evaluation index is proposed. Simulation and experimental results demonstrate that the proposed method improves the rebar plane stability from 36.2% to 76.4%, validating the rationality of the tying sequence and the effectiveness of the stability enhancement. Moreover, physical experiments further confirm the feasibility and practicality of the proposed algorithm, providing reliable technical support for automated rebar-tying operations.