In the actual communication environment, the Specific emitter identification (SEI) task often encounters Zero-Shot Learning (ZSL) problems. In the ZSL scenarios, the irrelevant characteristics may weaken the inter-class aggregation and split features of the same category into different clusters, which exacerbates the misjudgment. In this paper, based on the global modeling ability of graph convolutional network (GCN), we design a Neighbourhood grAph NetwOrk (NANO) to improve this situation, which consists of feature extraction and a GCN-based transformation network. To train this network, we define a neighborhood graph (NG), a weighting strategy, and a novel NG loss. Finally, experiments on practical collected signals demonstrate that DNG-Net outperforms discrete value detection methods in SEI tasks.