Moving object gathering pattern represents a group event or incident that involves congregation of moving objects, enabling the prediction of anomalies in traffic system. However, effectively and efficiently discovering the specific gathering pattern remains a challenging issue since the large number of moving objects generate high volume of trajectory data. In order to address this issue, this article proposes a moving object gathering pattern mining method that aims to support the mining of gathering patterns by using spatio-temporal graph. In this method, firstly an improved density based clustering algorithm (DBScan) is used to collect the moving object clusters. Then, a spatio-temporal graph is maintained rather than storing the spatial coordinates to obtain the spatio-temporal changes in real time. Finally, a gathering mining algorithm and its improved version are developed by searching the maximal complete graphs which meet the spatio-temporal constraints. The effectiveness and efficiency of the proposed methods are outperformed other existing methods on both real and large trajectory data.