Existing few-shot relational triple extraction methods often struggle with handling multiple triples in a single sentence and fail to consider the semantic similarity between the support set and the query set. To address these issues, this study proposes a few-shot relational triple extraction method based on module transfer and semantic similarity inference. The method uses a mechanism that constantly transfers among three modules, namely relation extraction, entity recognition, and triple discrimination, to extract multiple relational triples efficiently from a query instance. In the relation extraction module, BiLSTM and a self-attention mechanism are integrated to better capture the sequence information of the emergency plan text. In addition, a method based on semantic similarity inference is designed to recognize emergency organizational entities in sentences. Finally, extensive experiments are conducted on ERPs⁺, a dataset for emergency response plans. Experimental results show that the proposed model is more suitable for relational triple extraction in the field of emergency plans compared with other baseline models.