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The business activities of an enterprise are the unending development of the behaviours of groups, and the financial risks that are formed are a direct result of the business activities of groups constantly evolving. The process of identifying potential financial risks can be viewed as somewhat of a game between those with access to confidential information and those who actively seek it out. Techniques for mining extensive amounts of data, often known as big data mining, have the potential to improve organisational structure, increase managerial efficiency, and dramatically reduce financial risk. However, the traditional enterprise management idea and irrational internal business structures also contribute to some scenarios that are out of sync with the introduction of Big Data Mining technology. This results in some cases where the technology is ineffective. This topic is the starting point for research on an optimization model for the measurement of corporate financial risk that is based on Big Data Mining and secure neural networks. In this article, all internet users are considered to be "sensors" that are placed all over the internet by businesses. In order to train a Backpropagation Neural Network, you must first take a series of identical input samples and the ideal output as training "samples," and then you must introduce the network by a specific training algorithm. This allows the Backpropagation Neural Network to study the "solution," which contains the underlying principles. Establish a threat early warning indicator system, put Big Data Mining into action, assess the results, and publish a risk early warning report to assist enterprise management in making decisions.