Research focused on how to mine frequent daily behavior patterns of users on smartphones feasibly and efficiently was started. Firstly, a frequent behavior patterns mining framework based on smartphones was proposed. Secondly, a dynamic sliding window algorithm DSW(dynamic slide window) to decrease the context baskets in quantity and improve mining efficiency was proposed. Furthermore, a frequent patterns mining algorithm WePM(weighted pattern mining) which takes both frequency and duration of context occurrence into consideration was developed. On the basis of the above preparation, the mining framework and algorithm were verified experimentally with the context data from 21 users over 6 weeks. Results indicate that the proposed framework and frequent patterns mining algorithm can feasibly and efficiently run on resources limited smartphones to mine daily behavior patterns, and then to reflect users’ lifestyles. Finally, the patterns from two perspectives, namely behavior patterns in different locations and time periods are visualized, which benefits the users to realize their daily behavior patterns at any time.