Demographic prediction is a very important component to build mobile user profile that can help improve personalized services and targeted advertising. However, demographic information is often unavailable due to user privacy issue. This paper presents technologies and algorithms to build demographic prediction classifiers based on mobile user data such as call logs, app usages, Web data and so on. To associate those data with demographic information, we implemented a system that consists of two parts: mobile application for data collection with web infrastructure for user survey administration (i.e. gender, age, marital status and so on), and classifiers to predict demographic information. In the demographic prediction, we focus on user interest which is semantically extracted from Web data rather than other mobile data. To capture user interest more precisely, advanced topic model called ARTM (Additive Regularization of Topic Models) used. Using user interest as features, the experimental results show our system achieves demographic prediction accuracies on gender, marital status, and age as high as 97%, 94%, and 76%, respectively using deep learning.