Aimed at the problem that individual identification of communication radiation sources has a certain lack of label data under conditions of non-cooperative communication, a method of individual identification of communication emitter is proposed based on deep clustering. The powerful feature extraction and data reconstruction capabilities of the auto-encoder network are utilized for carrying out the representation learning of the original I/Q data, extracting the fingerprint features of individual recognition, and jointly optimizing the representation learning process and the feature clustering process, so as to achieve a higher fit between the representation learning and the feature clustering, and complete still greater individual identification of the communication emitter without labels. The experimental results show that the recognition accuracy is more than 85% when the SNR is above 0 dB. And the proposed method is valid and stable.