Aiming at generalized or fuzzy queries, the content of the returned list of Web search engines is clustered to help users to find the desired information quickly. Generaly, the returned list consists of short texts called snippets carring few information which traditional Term Frequency-Inverse Document Frequency (TF-IDF) feature selection model is not suitable for, so the clustering performance is very low. An effective way to solve this problem is to extend snippets according to a external knowledge base. Inspired by neural network based word presenting method, a new snippet extension approach based on Word2Vec model was proposed. In the model, Top N similar words in Word2Vec model were used to extend snippets and the extended text was able to improve the clustering performance of TF-IDF feature selection. Meanwhile,in order to reduce the impact of noise caused by some common used terms, the term frequency weight in TF-IDF matrix of the extended text was modified. The experiments were conducted on two open datasets OPD239 and SearchSnippets to compare the proposed method with pure snippets, Wordnet based and Wikipedia based feature extensions. The experimental results show that the proposed method outperforms other comparative methods significantly in term of clustering effect.