As an important information platform, micro-blog has a large number of user visits every day, and important public opinion events will form a hot topic on micro-blog. In this study, we propose a novel micro-blog topic detection method, named TDFWN (Topic detection in frequent word networks),to excavate hot topics in micro-blog corpus. First, frequent k-item sets (k≥3) in Microblog text data are mined. Second, a word co-occurrence network is build based on these mined frequent k-item sets. Third, the network is partitioned into different communities by using a community detection method, where each community represents a micro-blog hot topic. At last, the micro-blog text data are clustered into different groups by computing similarity of each micro-blog text with the found topics. The empirical study shows that the TDFWN method is able to find hot topics in micro-blog text data and cluster the micro-blog text data by the found topics simultaneously.