With the dramatic countrywide development of mobile internet, it becomes very important to extract valuable information from mobile device log data and report the analysis result through visualization method to help application developers and distributors maximize monetization opportunity. Currently, most of mobile log data analysis work is based on single dimension statistics, e.g., app download rank, and user retention rates. In order to mine deep information hiding behind mobile device log data and summarizes user characteristics. A method is proposed for analyzing users' characteristics and computing users' profile. An app topic model is constructed based on mobile log data, user clusters are build according to app topics, and two visualization methods are designed to show user characteristics clusters. Furthermore, user clusters are combined with time information and geographical information to show user characteristics from additional dimensions. Finally, a mobile log data visualization analysis B/S system is implemented to demonstrate the validity of the method by a case study.