With the development of mobile Internet, behavioral trajectories of human life are more and more recorded, which makes it possible to use computer technology to mine organizational behavior patterns. The mining of organizational behavior patterns based on social computing can not only prepare them in a targeted manner but also predict the consequences of possible measures. The organization behavior pattern mining has achieved a series of achievements in the fields of e-commerce and enterprise management. However, the problem of class imbalance and nonconsistent misclassification cost is common in the field of organizational behavior. For this problem, this project compares and analyzes the performance of the organizational behavior prediction model established by four typical cost-sensitive learning methods based on six classifiers, which provides a basis for the appropriate selection of cost-sensitive learning methods in different situations. Among them, the upsampling learning method is a better cost-sensitive learning method. However, there are some shortcomings in the upper sampling method. In order to avoid the possible overfitting problem of the social organization behavior prediction model established by the upper sampling method, this project proposes a new cost-sensitive learning method suitable for the mining of organizational behavior patterns. Based on the cost curve, this project proposes an effective personalized solution to the problem of class disequilibrium and nonconsistent misclassification cost in organizational behavior prediction modeling.