This paper applies the complex network analysis method to study the individual lending network characteristics on Internet financing platforms and borrower’s default problem.The paper proposes a method to establish the individual lending network, constructs the lending network of Renrendai.com, and depicts the features of the overall network and subnetworks that are separated by borrowers and pure investors according to their default status and credit ratings. Based on this, the behavior rules of borrowers and investors are discussed and the correlation between the network topology characteristics of borrowers and loan information is analyzed. Then three regression models for borrowers’network topology characteristics, loan information and their defaults are constructed. The results show that the network topology has a strong explanatory power for the structure of lendingborrowing relationship. There is a significant correlation between the network topology characteristics of borrower nodes and the borrowers’default, and this correlation is also robust among groups of borrowers with different credit ratings. These findings can be applied to borrower credit risk assessment and default prediction on Internet financing platforms.