Mining the semantics of the microblog texts to realize accurate search is an essential task in microblog search. Because the content of the short texts in microblog has the characteristics of sparsity and semantic limitation, the traditional search methods which only analyze the semantics of literal text for short texts understanding and similarity matching have certain restriction. Therefore, we propose an extended search algorithm based on social and conceptual semantics. By exploiting the unique social attributes such as the #hashtag#, the mention “@” and the link information URL in the social network, we further extend the short texts in microblog through the social semantics. The method combines the conceptual words obtained from literal analysis of short texts with the potential associated hashtags information in a graph structure formed by social relationships. It performs the feature representation of short texts in two semantic extensions and achieves the precise search based on full mining of short texts meaning. Finally, we conduct experimental comparisons with traditionally extended search algorithms in the microblog datasets. The results show that the proposed algorithm can capture more semantics and has semantic enhancement function in the search for short texts of microblog. Moreover, the search performance has been significantly improved in the short texts of microblog.