Entity linking includes entity discovery， query expansion， candidate generation， feature extraction and ranking. Here the query expansion method based on word embedding is proposed. Word embedding of words are trained by continuous bag-of-words (CBOW) model. Then the related words become the expansion words. The related words could make up the expansion based on rule. The related words could recall more and more candidate words simultaneously. In the feature extraction，the topic similarity between texts is extracted as the feature based on latent Dirichlet allocation(LDA). This paper extracts the synonyms based on word embedding as the dimension of text vector. Finally, learning to rank model is used to select the best candidate entity. The result shows that the method can ensure F1 reaching 0.71, and be effective for entity linking.