Aiming at the problem that specific emitter identification basically follows the closed set hypothesis and cannot effectively identify unknown classes, a specific emitter identification model based on zero sample learning is proposed to identify known and unknown specific emitter. By building a convolution neural network to extract the semantic features hidden under the emitter signal data, the attention module is introduced to enhance the focus on key features, and a combined loss function is proposed to separate different types of emitter signals in the semantic feature space, and the specific emitter classification and recognition are carried out according to the distribution of emitter signals in the semantic feature space. The experimental results show that compared with traditional closed set recognition, the proposed model can recognize unknown classes and distinguish between unknown classes while maintaining the recognition rate of known classes, with an average recognition rate of more than 90%. In engineering verification, the established unknown specific emitter identification platform can achieve fast and accurate recognition in indoor and outdoor scenes.