  With the rapid development of Internet finance, a large number of studies
have shown that Internet financial platforms have different financial systemic
risk characteristics when they are subject to macroeconomic shocks or fragile
internal crisis. From the perspective of regional development of Internet
finance, this paper uses t-SNE machine learning algorithm to obtain data mining
of China's Internet finance development index involving 31 provinces and 335
cities and regions. The conclusion of the peak and thick tail characteristics,
then proposed three classification risks of Internet financial systemic risk,
providing more regionally targeted recommendations for the systematic risk of
Internet finance.
