Aiming at the problem that the existing microblog event extraction methods are based on the content characteristics of events and ignore the relationship between the social attributes and time characteristics of events， so that they cannot identify the key events in the propagation process of microblog hot spots， a key event extraction method from microblog by integrating social influence and temporal distribution was proposed. Firstly， the social influence was modeled to present importance of microblog events. Secondly， the temporal characteristics of microblog events during evolution were considered to capture the differences of events under different temporal distributions. Finally， the key microblog events were extracted under different temporal distributions. Experimental results on real datasets show that the proposed method can effectively extract key events in microblog hot spots. Compared with four methods of random selection， Term Frequency-Inverse Document Frequency （TF-IDF）， minimum-weight connected dominating set and degree and clustering coefficient information， the proposed method has the event set integrity index improved by 21%， 18%， 26% and 30% on dataset 1 respectively， and 14%， 2%， 21% and 23% on dataset 2 respectively. The extraction effect of the proposed method is better than those of the traditional methods.