Specific emitter identification （SEI） refers to the technique of distinguishing emitters by utilizing unique and subtle features in received electromagnetic signals. Due to its powerful feature extraction ability， deep learning has gradually become the main means of implementing SEI. However， in non-cooperative scenarios， labeled samples generally cannot be obtained to train the neural network， and the number of emitters to be identified is unknown. Therefore， this paper proposes an unsupervised SEI method based on directed graph connectivity without specifying the number of emitters. Drawing inspiration from the idea of hierarchical clustering， the radio frequency fingerprinting feature space is first divided into multiple sub-clusters based on local density， and the relationship between feature vectors is mapped to a directed graph. Then， based on the connectivity of the directed graph， the multiple subclusters are automatically merged to obtain the final identification result. Experimental results show that under low signal-to-noise ratio conditions， the proposed method can accurately identify individual emitters， and its identification performance is improved by 7.1%—53.1% compared to the benchmark algorithms.