Abstract: wireless sensor networks (WSN) deployed in outdoor environments typically operate under harsh conditions and are vulnerable to network disconnection due to node failures or energy depletion. Therefore, network connectivity is essential for ensuring normal communication between sensor nodes. Considering scenarios where sensor nodes are randomly distributed in three-dimensional space, this paper proposed a UAV Swarm assisted connectivity enhancement (USACE) algorithm. The algorithm was introduced with an adaptive height optimization (AHO) algorithm, which was designed to enable the UAV swarm to autonomously adjust its flight altitude based on ground nodes' connectivity requirements, thereby expanding coverage area. An improved firefly algorithm (IFA) was employed to plan the initial path for visiting all nodes, followed by an innovative coverage-conscious route optimization (CCRO) algorithm that was developed to optimize flight trajectories by eliminating redundant coverage and reducing turns and path intersections. The objective was to minimize UAV swarm energy consumption while maximizing network connectivity time, achieving efficient coverage. The simulation results show that the USACE algorithm significantly outperforms the comparison schemes in both key indicators of path optimization and energy consumption control. At the same time, it demonstrates better stability and scalability, maintaining an average performance advantage of 19.5% even in a large-scale scenario with 8 unmanned aerial vehicles.