For the problem that closed set networks cannot effectively identify unknown classes in open set scenarios, an open set specific emitter identification(SEI )method based on the combination of deep learning and unsupervised clustering was proposed. The original data is refined to extract the conventional features containing fingerprint features, and the conventional feature waveform is trained in the closed set network，based on probability measures, the known and unknown decisions are carried out on the identification class samples, and unsupervised dimension reduction and clustering are carried out on the features of the full connection layer of the residual network extracted from the samples identified as unknown classes. The number of unknown individuals was clarified the clustering results. The experimental results show that compared with traditional closed set recognition, the proposed method can realize the effective classification between known classes and unknown classes ,under the known classes accurate recognition, the recognition accuracy is more than 97%, and ten unknown radios can be accurately clarified，experimental results verify the effectiveness of the method。