Business decision-making is a decision-making process that relies heavily on business data, and with the continuous improvement of information technology, the level of intelligence of business decision-making has been increasing. The paper suggests relevant optimization strategies for business decision-making and creates an intelligent business decision-making process that utilizes big data. This paper mainly applies the association rule mining algorithm based on big data to business decision-making, extracting valuable information for business decision-makers from a large database of business information systems. In order to verify the operational efficiency of the improved algorithm, the traditional Apriori algorithm, FP-Growth algorithm, HBE-Apriori algorithm, and the improved algorithm are compared at the same time, and the results of the experiments show that the improved algorithm’s operational efficiency in the process of finding the maximal frequent itemset is significantly improved compared with the other three algorithms, which provides a basis for the analysis of the effectiveness of the optimization of business decision-making. The example of optimization of business decisions through big data technology found that after the optimization of business decision optimization, the asset quality and solvency of the enterprise are improved, and the operating capacity is unchanged due to the low operating capacity of the total assets of the enterprise. This paper’s method for optimizing business decisions based on association rules is shown to be feasible.