News media and social networking sites present a great volume of spatio-temporal text data every day. It is difficult for people to get valuable event information. Aiming at the existing methods which depend on a large number of annotation data and consider the temporal elements and spatial elements of events in an isolated way, this paper proposes an event extraction approach based on spatio-temporal analysis. Firstly, a data cube structure was introduced to store event information. Users can extract important events based on different temporal and spatial granularity. Secondly, a real-time event clustering algorithm based on semantic similarity is proposed. In the clustering algorithm, a GloVe model was adopted to learn the vector space of words, so that the event elements which collected in the same event cluster have strong semantic relevance. According to a large number of unlabeled network text, the approach achieves a 77.4% value of F1 which indicates that it can achieve event extraction task based on spatio-temporal analysis.