Individual microblogs with large clustering correlation enable a quick understanding of  bloggers' professional interests and experiences. Existing short text clustering methods lack sufficient consideration of the correlation between semantics and sentences. We propose a novel individual microblog clustering method according to semantic correlation based on the HowNet. The main steps are as follows: (1) use the skipgram to train a large number of microblog texts to generate word vectors; (2) according to original semantic senses of words to eliminate ambiguity in the sentence; (3) calculate the similarity of words and sentences between microblogs respectively and get the correlation metrics; (4) cluster individual microblogs according to the microblog correlation. Experimental results show that the proposed clustering method outperforms the hierarchical clustering method and density clustering method.