Aiming at the need to discover user behavior characteristics and knowledge from moving trajectory data, a user behavior profiling method based on moving trajectory information was proposed. Firstly, the trajectory coordinates were preprocessed to clean out good data. Secondly, the travel rules and the points of interest of the user were found by means of stay points detection, staying points’ semantics and frequent pattern mining. In the aspect of predicting user trajectory information, Key Points Long Short-Term Memory Networks (KP-LSTM) was proposed to predict the user’s future travel location; then the user’s important attribute characteristics were taken through the user profiling, intuitively depicting the characteristics and patterns of users’ lives. Finally, the availability of the method was proved by experiments, and the prediction accuracy was better than the traditional Linear regression and LSTM neural network.