Abstract In the information dependent environment, research on web behaviors is an important topic with widespread needs, especially concerning the analysis and research of online behaviors of college students. College students are an important core group and a new force among Chinese netizens. There is great practical significance and social value to exploring and grasping Chinese college students° characters such as user behavior, interests, and needs. Visual analysis can directly display the overall distribution characteristics of user behavior and lay the foundation for further in-depth analyses. In this study, the college students° network access logs are considered as the analysis object to indicate the features of group behavior under multiple time granularities, namely, term, week, and hour. Meanwhile, on the basis of research using Markov chain, Gini-index, H-index, and other feature indicators, this work attempts to reveal college students° online characters of sequential behavior, user interest, and needs in various hour intervals, which provides a scientific reference to understand the nuances of college students' online life and support enterprise personalized services under the big data environment. In particular, H-index is applied to the website ranking algorithm of user interest and shows the value of classical informetrics analysis in the process of analysis and application of online user behavior, thus promoting the integration of different methods in applied information science.
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