RBF neural network model construction for enterprise financial big data analysis

The study builds a system of financial indicators first, and then uses the fast density peak clustering (FDPC) algorithm and the Adam algorithm to optimise the radial basis function (RBF) network to create a model for predicting financial risk.The results reveal that the initial accuracy of the FDPC Adam RBF model is higher than 60%, and it tends to converge at four iterations, resulting in an accuracy of 95.6%.The FDPC Adam RBF model achieved a minimum value of 0.183 in mean square error (MSE).In summary, it can be seen that the RBF neural network model for enterprise financial big data analysis is significantly better than other common neural network models in terms of computational efficiency and prediction accuracy, making it more suitable for deep analysis of financial data and risk warning.This conclusion provides strong support for the application of advanced artificial intelligence technology in the financial field.