The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generatedalso. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically,it is more and more difficult for existing rule-based expert systems and traditional machine learning modelsystems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequentlychanging their fraud methods. In this article, an intelligent Internet financial fraud detection is proposed to implement graph embedding algorithm Node2Vec to learn andrepresent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the machine learning models.