Under the condition of few-shot specific communication emitter identification， the difficulty of extracting individual features of communication radiation source by the existing deep learning algorithm increases， and the recognition rate decreases. To solve this problem， this paper proposes a recognition method to construct a shallow neural network by fusing attention mechanism and broad learning. Firstly， broad learning is introduced to simplify the network model and reduce the overfitting phenomenon caused by small samples. Secondly， the node attention module is constructed to improve the feature extraction ability of the broad neural network under the condition of small samples. Finally， the effectiveness of the proposed method is verified on the public dataset. The results show that compared with the deep learning method with a small number of samples， the proposed method improves the overfitting phenomenon of the deep learning network， strengthens the feature extraction ability of the broad learning method， and improves the recognition accuracy.