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With the rapid development of Internet finance, a large number of studieshave shown that Internet financial platforms have different financial systemicrisk characteristics when they are subject to macroeconomic shocks or fragileinternal crisis. From the perspective of regional development of Internetfinance, this paper uses t-SNE machine learning algorithm to obtain data miningof China's Internet finance development index involving 31 provinces and 335cities 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 ofInternet finance.

Systemic Risk Clustering of China Internet Financial Based on t-SNE Machine Learning Algorithm