With the development of indoor positioning technology, a large amount of indoor location data and user data for consumer behavior makes the indoor Location Big Data (LBD) research and application possible. High-precision indoor location technology breaks the bottleneck of indoor location data with low accuracy. By clustering the indoor location data and dimension reduction pretreatment, a mining model was set up to extract the characteristics of custom and flow in the indoor shopping area. Then using the associated user consumption behavior to predict the characteristics of consumer behaviors, a collaborative mining method and architecture for large data of indoor location was put forward. Experiments on location dataset of billions of users in an airport and a shopping mall in Xidan were conducted. The results verify the accuracy and feasibility of the mining method based on this architecture of indoor positioning data.