To address the issues of path redundancy and low coverage in intelligent cleaning robot path planning, a hybrid algorithm for full coverage path planning is proposed, which integrates the semi-spring artificial potential field method, A* algorithm, and dynamic update strategy. This aims to solve the problem effectively. The A* algorithm is used for initial path planning, while the semi-spring artificial potential field method is employed for local obstacle avoidance to reduce local optima issues. The dynamic coverage value update strategy optimizes path priority based on real-time coverage, improving coverage efficiency and reducing redundant coverage. Additionally, the dynamic weight adjustment mechanism based on fuzzy logic enables the algorithm to adaptively adjust coverage and obstacle avoidance weights in complex environments. Simulation results show that the hybrid algorithm outperforms the traditional artificial potential field-based comparison algorithm and the comparison algorithm without dynamic coverage value update strategy in scenarios with regular obstacle distribution. Specifically, path length is reduced by 2.8% and 7.1%, and redundant coverage is reduced by 49.6% and 71.3%, respectively. In scenarios with irregular obstacle distribution, path length is reduced by 12.7% and 29.4%, and redundant coverage is reduced by 36.7% and 60.2%, respectively. In narrow road scenarios, path length is reduced by 32.8% and 25.5%, redundant coverage is reduced by 66.7% and 60.8%, and coverage rate is improved by 3.6% and 8.1%.