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Personal credit evaluation is one of the important means of financial risk prediction.Ttraditional method of personal credit evaluation is Single model analysis. In order to accurately evaluate personal credit and reduce the default loss caused by credit economy to internet finance, combinatorial thinking is needed. In this paper, SVM model and Logistic regression model are analyzed by single analysis, and we set up SVM-Logistic combination model. The results show that the SVM-Logistic model has higher robustness andaccuracy. Introduction In recent years, with the rapid development of information technology, "Internet +" has become more and more popular. More and more measurement methods have been applied to the field of credit evaluation. The rapid development of big data provides more basic conditions for credit evaluation.A single model has its own limitations. Based on a single model, some scholars make use of the advantages of multiple models and synthesize the advantages of various aspects to combine two or more models to evaluate personal credit. This reduces the probability of errors to a certain extent, and improves the accuracy and stability of the evaluation model.Jiang Based on the advantages of different single models in personal credit evaluation, Jiang Minghui[1] established the Logistic-RBF combination model, and verified the advantages of the combination model. Xianghui and Yang Shenggang[2] propose a method of building composite model based on multi-classifier. Based on Support Vector Machine (SVM) and Logistic regression model, this paper establishes a SVM-Logistic combination model based on group feature characterization, evaluates and analyses personal credit, and cross-validates the results in order to improve the accuracy and robustness of the model. Analysis Modeling Support Vector Machine Let the training sample set be   , , 1, 2, , , i i x y i N  L is introduced as model input, i Y R  is the corresponding output variable. The corresponding linear regression function can be defined as:     f X w X b     (1) Where, w is the weight vector, b is bias. According to statistical theory, the fitting problem of equation (1) can be described as the following optimization problem: N i e b x w y t s e w e w

Personal Credit Evaluation Under the Big Data and Internet Background Based on Group Character