Aiming at the characteristics of large passenger flow, complex and variable indoor environment in airport terminals, an indoor positioning algorithm based on Spectral Regression Kernel Discriminant Analysis (SRKDA) was proposed. In the offline phase, the Received Signal Strength (RSS) data of known location was collected, and the non-linear features of the Original Location Fingerprint (OLF) were extracted by SRKDA algorithm to generate a new feature fingerprint database. In the online phase, SRKDA was firstly used to process the RSS data of the point to be positioned, and then Weighted K-Nearest Neighbor (W KNN) algorithm was used to estimate the position. In positioning simulation experiments, the Cumulative Distribution Function (CDF) and positioning accuracies of the proposed algorithm under 1.5 m positioning accuracy are 91.2% and 88.25% respectively in two different localization scenarios, which are 16.7 percentage points and 18.64 percentage points higher than those of the Kernel Principal Component Analysis (KPCA)+W KNN model, 3.5 percentage points and and 9.07 percentage points higher than those of the KDA+W KNN model. In the case of a large number of offline samples (more than 1100), the data processing time of the proposed algorithm is much shorter than that of KPCA and KDA. The experimental results show that, the proposed algorithm can effectively improve the indoor positioning accuracy, save data processing time and enhance the positioning efficiency.

Accurate classification of users plays an important role in improving the quality of customized services, but for privacy considerations users, often do not meet the network service providers, refusing to provide personal information, such as location information, hobbies and so on. To solve this problem, by analyzing the multi-layer network traffic such as network layer and application layer under the premise of protecting user privacy, and then using machine learning methods such as K-means clustering and random forest algorithm to predict the user's geographic location types (such as apartments, campuses, etc.) and hobbies, and the relationship between geographic location types and the user interests was analyzed to improve the accuracy of user classification. The experimental results show that the proposed scheme can adaptively partition the user types and geographic location types, and improve the accuracy of user behavior analysis by correlating the user's geographic location type and the user type.