Natural　disasters　often　have　an　unpredictable　impact　on　human　society　and　can　even　cause　significant　problems,　such　as　damage　to　communication　equipment　in　disaster　areas.　In　such　post-disaster　emergency　rescue　situations,　unmanned　aerial　vehicles　(UAVs)　are　considered　an　effective　tool　by　virtue　of　high　mobility,　easy　deployment,　and　flexible　communication.　However,　the　limited　size　of　UAVs　leads　to　bottlenecks　in　battery　capacity　and　computational　power,　making　it　challenging　to　perform　overly　complex　computational　tasks.　In　this　paper,　we　propose　a　UAV　cluster-assisted　task-offloading　model　for　disaster　areas,　by　adopting　UAV　clusters　as　aerial　mobile　edge　servers　to　provide　task-offloading　services　for　ground　users.　In　addition,　we　also　propose　a　deep　reinforcement　learning-based　UAV　cluster-assisted　task-offloading　algorithm　(DRL-UCTO).　By　modeling　the　energy　efficiency　optimization　problem　of　the　system　model　as　a　Markov　decision　process　and　jointly　optimizing　the　UAV　flight　trajectory　and　task-offloading　policy　to　maximize　the　reward　value,　DRL-UCTO　can　effectively　improve　the　energy　use　efficiency　of　UAVs　under　limited-resource　conditions.　The　simulation　results　show　that　the　DRL-UCTO　algorithm　improves　the　UAV　energy　efficiency　by　about　79.6%　and　301.1%　compared　with　the　DQN　and　Greedy　algorithms,　respectively.