A trustworthy interaction detection method based on user behavior flows is proposed to ensure the dependability and trustworthiness of human-web interaction. Firstly, the behavior flow diagram is used to capture the all relevant factors of user behavior from web log, in which the uniqueness is taken as the starting point of this. The behavior units are recorded as “one session”. The behavior flow diagram describes the interactions in four dimensions, namely interactive environment, interactive tool, session behavior, and the current page. Then, the behavior features related to individual psychology and physiology are extracted as trustworthiness measures on the basis of data analysis. After balancing the data set through synthetic minority over-sampling technique(SMOTE), the training and testing trustworthy interaction detection model are completed by the aid of the decision tree and random forest algorithm. Finally, an instance is given to illustrate that the false accept rate(FAR) of the untrustworthy behavior of the proposed method in decision tree model is 0.44%, while it is as low as 0.31% in random forest. The results indicate that the combination of trustworthy behavior features has differentiation and uniqueness among users, which proves that the behavior patterns of human-web interaction have personality and distinguishable otherness with someone else. It can be used to detect identity consistency between the dominator of interactive behavior and the real owner of an account.