Indoor location has become the core part for large-scale location-aware services, especially in scalable applications. Fingerprint location is carried out by using the received signal strength indicator (RSSI) of WiFi signal, which has the advantages of full coverage and strong expansibility. At the same time, it also has the shortcomings of off-line data calibration and insufficient samples in dynamic environment. In order to locate the hierarchical information of the user's building, floor and space, a deep neural network for indoor positioning (DNNIP) is explored using stacked auto-encoder and data stratification. Experimental results show that DNNIP has better classification accuracy than other machine learning algorithms based on UJIIndoorLoc dataset.