Abstract: To solve the problem of the missing of the effective scores of online personal credits, the characteristics of internet personal credit assessment data are analyzed. Support vector machine (SVM), random forest (RF), and XGBoost have been adopted to establish the credit forecasting model in the paper, respectively.The voting fusion of the proposed models is conducted. Based on the data characteristics of internet credit data, discretization, normalization, and feature combination are adopted to experimental data set in feature engineering. In order to improve the contrast, the logistic regression analysis-the core of FICO assessment is carried out. The experimental results show that the performance of the three established algorithm are better than logistic regression. XGBoost performs better than SVM and RF model in the accuracy prediction. The performance of voting fusion model is better than that of single model, with outstanding model resolution and prediction accuracy, which is more suitable for internet personal credit assessment.