Summary As human activities in mobile environments are facing an ever‐increasing range of data, it is of great research importance to explore in depth the salient features of data that discriminate human behavior patterns and socio‐demographic factors. However, human activity behavior that consists of a series of complex spatio‐temporal activity processes is difficult to model. In this article, we develop a framework to perform activity behavior pattern mining and recognition, and the proposed framework has been applied to the American Time Use Survey to explore representative activity behavior patterns and socio‐demographic factors. The main contributions are as follows: (1) A method of activity behavior similarity is presented based on daily activities and activity sequences. (2) An activity sequence similarity algorithm with O(p(m−p)) is proposed by line segment tree, greedy algorithm, and dynamic programming. (3) Representative activity behavior patterns and socio‐demographic factors are derived by clustering analysis and mining. (4) The activity behavior pattern is recognized by activity behavior features or socio‐demographic features. Through the experiments, we find that different daily activity behavior patterns are associated with specific socio‐demographic factors.

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