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Aiming at the problem of low recognition accuracy in the actual environment where the number of labeled signal samples is small and the distribution of the signals to be identified is changing due to the actual channel, the adversarial-based consistency egularization semi-supervised emitter identification method was proposed. The method introduces the concept of adversarial-based domain adaptation in the consistency egularization semi-supervised method firstly, and establishes a network model to extract "domain-invariant" features, i.e., the alignment of signal features under different signal-to-noise conditions, so that the improved semi-supervised model trained on the original signal can achieve high accuracy recognition of signals under other signal-to-noise ratios. Experiments are conducted on the ORACLE RF fingerprint dataset with different training set settings, and the experimental results show that the adversarial-based consistency egularization semi-supervised emitter identification method has a higher recognition accuracy than the fully-supervised method and the onsistency egularization semi-supervised method in the actual scenario.