Conventional multiple backup methods often rely on fixed backup strategies and cycles, and lack real-time awareness and dynamic adjustment capabilities for data changes, resulting in high space occupancy. In order to solve this problem, a multiple backup method of enterprise financial data based on active learning algorithm is designed. Based on active learning algorithm, it can effectively calculate the similarity of enterprise financial data, so as to accurately identify redundant or duplicate data. Through deduplication of these data, it ensures that the data in the backup process is clear and accurate, avoids the storage of invalid data, and realizes multiple backup of enterprise financial data. The comparison experiment shows that when the data volume is 500MB, the space occupancy rate of method 1 is 95%, and that of method 2 is 96%, while the designed method remains relatively low at 42%. This result shows that the space occupation rate of the designed method is the lowest, which can further improve the speed of multiple backup of enterprise financial data, and achieve a significant improvement in the performance of multiple backup of enterprise financial data.