<p>Zero-shot relation extraction based on semantic matching is one of the hot topics in natural language processing. However, on the one hand, simply using relation descriptions and sentences for matching leads to insufficient semantic information; on the other hand, not all parts of a text contribute equally to relation extraction. Therefore, a zero-shot relation extraction method based on self-attention mechanism and semantic matching (SASM) is proposed. In order to enrich the semantics of relation descriptions, SASM proposes to sums up synonyms of relations with relation descriptions to obtain relation vectors. At the same time, it adopts self-attention mechanism to obtain key contextual information in sentences, and then concatenates the two entities to obtain sentence vectors. Then, jointly minimizing the distance between sentence vectors and relationship vectors to classify the seen relationships. During testing, generate embeddings of unseen relations and new sentences, and use nearest neighbor search to predict unseen relation. The F1 value on two public datasets increased by 0.39 and 0.52, respectively. Synonyms can effectively expand the semantics of relations, and self-attention mechanism help accurately locate key information in sentences. Combining them can effectively improve the performance of semantic matching-based zero-shot relation extraction. We release our code at <a href="https://github.com/lululu666123/yang_second_work/tree/master">https://github.com/lululu666123/yang_second_work/tree/master</a>.</p>