The traditional statistical analysis method presents numerous disadvantages, making it difficult for enterprises to manage and analyze large amounts of economic data. It also overlooks the impact of time continuity on the economy, which hinders management from making timely and accurate decisions. This paper discusses the feasibility and principles of applying big data, cloud computing, and other intelligent technologies to innovation in enterprise economic management. It analyzes the role of data mining technology in improving enterprise economic management in the new business environment. An intelligent economic management system consisting of five subsystems, namely budget management, performance management, cost management, risk management, and decision support, is being constructed. The Apriori association rule algorithm is utilized to mine enterprise-to-economic management risk data, and by using confidence and support, the extraction of risk data from the enterprise economic management system is realized to provide data support for economic strategic decision-making. Taking Company P as the research object, mining and analyzing the economic data of the enterprise from 2019 to 2023 by using the construction of an intelligent economic management system found that when the support degree threshold is taken as 0.6 and the confidence degree threshold is taken as 0.3, the company has as many as 84 risks to economic management in the research period. This paper’s method effectively extracts the unfavorable risks to the enterprise from the economic data, providing management with data references for decision-making support.