To address the problems of low exploration efficiency and information exchange under limited communication bandwidth in the current Multiple Unmanned Aerial Vehicle （Multi-UAV） systems when exploring large-scale complex environments， a fast and fully autonomous exploration method for Multi-UAV in large-scale complex environments was proposed， including a fast and hierarchical exploration strategy and a lightweight large-scale environment modeling method. Firstly， closed viewpoints were generated in the front-end trajectory planning part to drive the Unmanned Aerial Vehicles （UAVs） to explore unknown environments. Then， the smooth， continuous， and time-optimal trajectory optimization problem was transformed into a convex optimization problem in the back-end， and this problem was modeled systematically. Meanwhile， in terms of environmental characterization， a random mapping method was used for lightweight mapping and map data interaction. Finally， in simulation， the proposed method was compared with fast exploration method using incremental boundary information and hierarchical planning — FUEL （Fast Unmanned aerial vehicle ExpLoration）， rapid exploration method based on frontiers — FBE （Frontier-Based Exploration）， and exploration method based on the next best viewpoint — NBVP （Next Best View Planner）. The results show that the proposed method improves the exploration time performance by 14.4%， 43.9% and 47.7%， respectively， and the lightweight mapping method reduces the data size by 28.3% and 22.4%， respectively， compared to the Bayesian method and the polyhedron method. It can be seen that the proposed method can perform fast and fully autonomous exploration in large-scale complex environments efficiently.