To solve the problem that the traditional query expansion used in professional domains suffers from the lack of semantic relations between expansion terms and original queries, a query expansion approach based on semantic vector representation was proposed. First, a semantic vector representation model was designed to learn the semantic vector representations of words from their contexts in corpus. Then, the similarities between words were computed with their semantic representations. Afterwards, the most similar words were selected from the corpus as the expansion terms to enrich the queries. Finally, a search system of biomedical literatures was built based on this expansion approach and compared with the traditional query expansion approaches based on Wikipedia or WordNet and the BioASQ participants along with the significant difference analysis. The comparison experimental results indicate that the proposed query expansion approach based on semantic vector representations outperforms the baselines, and the mean average precision increases by at least one percentage point; furthermore, the search system performs better than the BioASQ participants significantly.