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Summary Based on the analysis of the existing traditional supply chain financial model, this article proposes an edge intelligence‐enabled supply chain financial model based on Business‐to‐Business (B2B) platforms, combines the operation mechanism of the model and the quantitative analysis thinking of the traditional supply chain financing. This article uses the model to construct and evaluates the cost–benefit model of dealers, manufacturers, and B2B e‐business platforms in the perspective of the supply chain finance for B2B platforms. In order to further explore the operation strategy of supply chain member enterprises, this article also explores the selection of financing objects of B2B platforms, the optimization of financing cost rate formulated by the loan amount of financing enterprises, and the optimization of product order quantity. Numerical analysis shows that the proposed model can directly reflect the changing trend of the relevant parameters of B2B platforms' supply chain financial model and provide suggestions for the cooperation mechanism between supply chain and commercial banks.

With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.

With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.