With the development of networked and intelligent unmanned aerial vehicles （UAVs）， they have gradually become an important component of the low-altitude intelligent network （LAIN）. However， the effective management of UAV platforms in the LAIN still faces severe challenges. Based on the subtle features of UAV signals， individual identification of UAVs can be achieved， and illegal UAVs can be detected， thereby realizing the identification and management of UAVs in the LAIN. In response to the problem of complex channel environments and the inability to obtain illegal UAV signal samples in advance in the low-altitude domain， this paper proposes an open set identification method for UAVs based on differential time-frequency and multi-center OpenMax. Firstly， this paper proposes channel-independent differential time-frequency features to reduce the impact of multipath channel environments on radio frequency fingerprinting （RFF） features and uses data augmentation to improve the accuracy and robustness of the identification model. Secondly， this paper uses multi-center OpenMax to replace the neural network’s SoftMax layer for open set identification of UAVs. Finally， the loss function of the neural network is improved to increase the accuracy of open set recognition. The proposed algorithm is validated using real-world data. When the openness is 0.087， the open set recognition accuracy reaches 93.23%， an increase of 7.61% and 13.4% compared with the benchmark algorithms. The algorithm proposed in this paper can effectively identify individual UAVs and detect illegal UAVs appearing for the first time in complex channel environments.