In the online loan user data set, there is a serious imbalance between the number of successful and failed loan users. The traditional machine learning algorithm pays attention to the overall classification accuracy when solving such problems, which leads to lower prediction accuracy of successful loan users. In order to solve this problem, the class distribution was added to the calculation of cost-sensitive decision tree sensitivity function, in order to weaken the impact of positive and negative samples on the misclassification cost, and an improved cost-sensitive decision tree based on ID3 (ID3cs)was constructed. With the improved cost-sensitive decision tree as the base classifier and the classification accuracy as the criterion, the base classifiers with better performance were selected and integrated with the classifier generated in the last stage to obtain the final classifier. Experimental results show that compared with the existing algorithms to solve such problems (such as MetaCost algorithm, cost-sensitive decision tree, AdaCost algorithm), the improved cost-sensitive decision tree can reduce the overall misclassification rate of online loan users and has stronger generalization ability.