To enhance the sustained mission capability of large-scale UAV swarms facing both soft and hard-kill threats, this research focuses on swarm topology reconstruction methods. Addressing the issue of node failures, a dynamic control authority migration mechanism under a centralized-distributed hybrid control architecture is proposed to improve reconstruction efficiency; concurrently, an SDN-based elastic networking architecture is designed, integrating intent-driven principles to enable intelligent and dynamic configuration of network resources. Simulation comparisons were conducted between dual-mode reconstruction strategies: neighbor autonomous compensation and resource-pool dynamic scheduling. When facing small-scale node loss, neighbor compensation leverages its advantage in local decision-making, reducing the average reconstruction latency by 38.5% compared to resource-pool scheduling. However, as the number of failed nodes increases, the combined centralized-distributed controller strategy achieves the shortest reconstruction time; notably, under persistent electromagnetic interference environments, this combined strategy demonstrates significant advantages, reducing latency by 21.7% compared to a purely distributed approach. This research provides theoretical support and practical reference for topology reconstruction in large-scale UAV swarms.