The construction of a smart campus has become an important part of educational informatization and a significant indicator of the educational modernization. Campuses across China are moving from traditional digital campus construction to smart campus construction. Chinese telecom operators, with their innate advantages in network infrastructure and resource, have become important service providers in the construction of smart campus. Telecom operators have effectively explored the application platform and application scenarios of smart campuses by leveraging existing educational service products and big data resources at their disposal.

This paper mines user behavior characteristics based on big data technology. This paper proposes a method for behavior slicing based on historical activity data, and insights into the personalized behavior characteristics. Firstly, the data is processed, and the classification is expanded on the basis of the parsed APP label types. Secondly, a time slicing method is proposed to reduce information loss, which integrates time, location, business type, and behavior into individual users. Then, based on slices of a day and a week, the paper analyzes user behavior and construct a portrait of user’s interest and preference. Finally, the periodic factor method is utilized to predict the behavior changes, forming the feature labels for users. Based on real business behaviors, this paper provides insight into user personality and effectively improves the authenticity and accuracy of prediction.