Abstract: Social networks are becoming more and more popular where people can post anything anytime. Due to the huge user community, social network data is increasing with each passing day. Therefore how to explore the knowledge in huge data seems to be hard work. As microblog has time-related characteristics and social network behavior attributes, momentum signal enhancement model is put forward to detect bursty microblog topics effectively. Influence factor and hot energy factor are put forward to improve the momentum model. The influence factor uses the data before the current point but within a given period to calculate the