BBS(Bulletin Board Systems) is one of the most common places for threaded discussion. It becomes more and more popular among web users, especially in China. Everyday a huge amount of new discussions are generated on BBS. It is too difficult to find hot topics. To solve this issue, we propose a novel approach to detect hot topics on BBS for any period of time. Our solution consists of three steps. First of all, candidate topics are extracted using the clustering method. Secondly, based on the extracted topics, aging theory is employed to valuate the hotness of topics. Both two steps above are carried out incrementally over time. Finally, topics are ranked and hot topics are detected. Experiments performed on practical BBS data show that our method is quite effective.