This letter proposes a semi-supervised identification method to address the challenge of ineffective identification of unknown radiation sources in communication emitter individual identification. The method involves multi-feature transformation and feature fusion of radiation source signals, followed by training an optimal closed-set model and feature extractor using a ConvNeXt network combined with an attention mechanism. The K-means clustering algorithm is then employed to classify and identify unknown radiation source signals. The effectiveness of the proposed method is verified through various evaluation metrics and feature visualization. Experimental results demonstrate that the method achieves a classification recognition rate of up to 95% for three types of unknown radiation source signals and exceeds 70% for four types, further confirming its effectiveness in the classification and identification of unknown radiation sources.