With the widespread use of personal mobile communication devices and location-aware devices, the mobile communication service provider has accumulated a lot of its users' location data. At present, most researches on location data are focused on the mining of active trajectories. A small amount of researches on the pattern of personal stay only determine activity stops, but lack further mining. We conduct researches based on the base station data and propose a simple method to identify the activity stops according to the characteristics of the base station data. Then we propose two methods for mining the pattern of personal stay. Finally, the real data are used to verify the effectiveness of the algorithm.