As a new and vigorous Internet financial model, Internet lending improves the efficiency of the use of financial resources and alleviates the financing difficulties of small enterprises. However, compared with traditional credit methods, the information asymmetry of online lending is more serious due to the low threshold of loans and the lack of real contact between borrowers and lenders, which leads to frequent platform defaults and increased credit risk. It is of great significance for the healthy and sustainable development of Internet finance to construct a set of reasonable credit evaluation index system and scientifically evaluate its credit risk status, so as to screen and prevent the potential risk of Internet lending in time. Based on K-S test and rank correlation analysis, this paper screens out indicators with strong ability to identify default status of borrowers, establishes an evaluation index system of online lending credit, and conducts an empirical study on the actual transaction data of Lending Club, the largest P2P network lending platform in the world. The results show that the amount of borrowing, the occupation of borrowers and the unemployment rate in the evaluation index system of this study. The 12 indicators have a significant impact on distinguishing default status. One of the characteristics and innovations of this paper is that the larger the statistical value of K-S test is, the larger the deviation between the distribution function of corresponding default samples and the distribution function of non-default samples is. It shows that the stronger the ability of the evaluation index to discriminate the default status of borrowers, the better the evaluation index can be selected to distinguish the default status from the non-default status. Foot. The second is to use K-S test and rank correlation analysis, which have no requirement on the overall distribution of evaluation indicators and are suitable for the non-parametric statistical method with unknown specific distribution to screen the evaluation indicators of network lending credit, to overcome the drawbacks of existing index screening methods that require the evaluation indicators to obey the strict requirement of normal distribution, and to expand the scope of application of index screening methods.