Abstract: To dig out the implicit meanings in user’s multi-behavior sequences, a new approach of mining personalized user profiles is proposed. Firstly, the method is presented to mine user’s interesting points and interesting vectors. A user’s interesting profile is obtained by combining the interesting point group with interesting vector group together, which is denoted by a weighted directed graph. Then, an algorithm is proposed to calculate the similarity between such user profiles. To verify the effectiveness of the approach proposed in this study, personalized recommendation experiments are realized by using content-based filtering and collaborative filtering, respectively. The results show that the average not acceptance rates of these recommendation services are only 5.94% using content-based filtering recommendation and 3.7% using collaborative filtering. It indicates that the approach proposed in this study is quite available in mining personalized user profiles.