With the rapid development of social networks and Internet， a large number of microblog short text stream data have been produced. Discovering hot topics from microblog text streams in time plays an important role in topic recommendation and public opinion monitoring. To solve the problem of sparse features of microblog， a feature extension-based hot topic detection （FE-HTD） method in microblog short text stream is proposed by using microblog comments to extend the features of microblog. To complete the feature extension of the microblog text， firstly， the comment text is selected by the influence of the comment users and the number of likes for comment text， and the feature words are extracted from the comment text by word co-occurrence and term frequency-inverse document frequency （TF-IDF） method. Then count the word pair speed， word pair acceleration and microblog text strength of the microblog short text stream. The burst feature is calculated by word pair acceleration and microblog text strength. Finally， the variable length window range of hot topic is determined according to the speed of the burst word pair， and the topic structure of hot topic in the window is obtained by clustering. In the experiment， the proposed algorithm is compared with the text-based topic detection （T-TD） method and the burst words-based topic detection （BW-TD） method. The results show that the accuracy of the proposed algorithm is 76.4%， and the recall rate is 78.7%，which are 10% higher than those of T-TD and BW-TD methods.