When dealing with incoming Unmanned Aerial Vehicles （UAVs）， it is crucial to recognize the formation of enemy UAVs quickly and accurately， in order to analyze and judge enemies’ combat intentions and formulate effective countermeasures. Therefore， a UAV swarm formation recognition algorithm based on multi-scale complex networks was proposed. Firstly， an adaptive threshold method was established to construct multi-scale complex networks using the UAV swarm formation， and the combination of eigenvalues corresponding to the adjacency matrices of these complex networks was selected to form a shape signature. Then， by introducing Hellinger distance to measure the difference between the shape signature of the formation to be recognized and the standard formation， so as to obtain the recognition results. Simulation results show that compared with the algorithm of obtaining multi-scale complex networks with hard thresholds， the proposed algorithm has better adaptability and robustness， has a higher recognition rate even when the target information is heavily corrupted， and has fewer parameters and lower time complexity.