针对网络个人信用有效评分缺失的问题，分析了互联网信贷个人信用评估数据的特点，选用支持向量机、随机森林和XGBoost分别建立了信用预测模型，并对3种单一模型进行了投票加权融合. 基于互联网信贷数据的特点，在特征工程中对样本集特征进行了离散化、归一化和特征组合等处理. 为增加对比，对实验数据集进行了FICO评估核心Logistic回归分析. 实验结果表明：3种单一算法性能均优于Logistic回归，XGBoost表现优于支持向量机和随机森林模型，预测相对准确；投票融合模型的表现比单一模型更好，模型分辨能力更优秀，预测精度更高，更适用于互联网信贷个人信用评估.
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.