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With the rapid e-commerce economic development, many small and medium-sized enterprises (SMEs) have thrived and aroused increasing attention from the state government. The financing problem of SMEs has naturally become a matter of concern. Under the circumstance, e-commerce finance has opened up new ways for solving financing difficulties faced by SMEs. However, due to the unstable development of SMEs and the imperfect network financing platforms and legal measures, the financing risks of SMEs can hardly be ignored. Given the trend of fast development of e-commerce, many e-commerce enterprises, led by Alibaba, have also been involved in microfinance, leading the trend of the combination of e-commerce finance and creating a new financial model combining e-commerce and microfinance. It is a new and fast way of loan and has mastered comprehensive and rich commercial data and advanced technologies. As a new force in financial systems, microfinance companies have been gradually emerging and growing. The diversified development trend has promoted the solution to the financing difficulties of SMEs. This paper expounds on the development prospects of SMEs and e-commerce finance and illustrates the significance of developing online finance. It also introduces the commonly-used research methods of the two kinds of financial models, such as multiple linear regression method and logistic regression method, and analyzes the reasons for the financing difficulties of SMEs. Currently, the high financing cost is the main reason for the financing difficulties of SMEs. Several reasons account for the high financing cost. Among them, high financing cost, low-efficiency financial system, long financing cycle, and the loan information asymmetry account for 35%, 21%, 19%, and 25%, respectively. In addition, this paper clarifies the advantages and disadvantages of network finance and the necessity of developing online finance.

Construction of a New Financial E-Commerce Model for Small and Medium-Sized Enterprise Financing Based on Multiple Linear Logistic Regression