Based on DL theory, this paper discusses and studies the early warning of enterprise financial risks in detail. And put forward a new enterprise financial risk early-warning model. The purpose is to enable enterprises to better analyze the changing trend of financial data, make correct decisions by managers and investors of enterprises, and promote the stable development of national economy and enterprises. This model is based on the early-warning theory of enterprises, based on the financial statements, business plans, and other relevant accounting information of enterprises, using accounting, finance, and marketing theories, adopting the methods of ratio analysis, comparative analysis, factor analysis, etc., to warn the financial risks of enterprises. This paper uses a lot of data to train the parameters of the DL financial early-warning model and then verifies the established financial early-warning model. In order to verify the reliability of this model, this model is compared with other two financial early-warning models. The results show that the prediction accuracy of this model is as high as 94%, which is 8~15% higher than that of other models. In this paper, the DL method has been applied to financial risk early warning and achieved good results. It has certain theoretical and practical significance in the field of enterprise financial early warning.

Multisource information mining systems and related business intelligence technology are currently a hot topic of research. However, the current commercial applications and applications are not ideal in terms of application. Because there is still much work to be done before decision support, it is best to transition to them only financially. This paper examines the multisource part of the information used in mining and introduces research hotspots in the fields of accounting informatization, the development status of intelligent financial analysis software, the research and application status of data warehouse, data mining, and decision support systems. This paper examines the specific composition and content of a financial information system using information mining to lay a solid foundation. Financial intelligent analysis, financial intelligent monitoring, financial intelligent decision-making, and financial intelligent early warning are the four parts of the financial intelligent system. It then examined the structure and processing of the financial intelligence system and proposed a financial intelligence system operation strategy. Financial intelligence low-risk integrated implementation strategies and ideal financial intelligence models, according to the current state of research and practical applications. According to the findings, the overall discrimination accuracy of the financial information system based on mining multisource information features is up to 95%, which is 42% higher than the traditional model. The development and use of financial information benefit from the realization and exploration of the financial intelligence system model.