With the continuous expansion of enterprise business scale, the amount of financial data is growing exponentially, and massive data has become the core resource for enterprise development. However, many enterprises have not yet established a comprehensive intelligent data analysis system, resulting in significant challenges in daily data collection and frequent loss of critical financial data, which seriously restricts the further development of enterprises. Although existing research has explored financial data processing, there are still shortcomings in the combination of algorithm efficiency and system practicality. For example, some studies focus too much on theoretical model construction, resulting in low data processing efficiency in practical applications; some systems struggle to meet the accuracy and automation requirements of enterprise analysis when faced with complex and heterogeneous financial data. In the field of intelligent computing, the chaos particle swarm optimization (PSO) algorithm provides a new approach for financial data classification and automatic analysis and processing due to its global search capability and parallel computing advantages. This article is based on the chaos particle swarm optimization algorithm, and deeply studies and designs an intelligent financial data processing system. By proposing adaptive particle swarm optimization (APSO) and simplified adaptive particle swarm optimization (RAPSO) algorithms, the data processing flow is optimized. The research results show that the system significantly improves the efficiency and automation level of financial data processing. 52% of enterprises believe that the automation level of system data analysis is high, and the RAPSO algorithm significantly shortens the data processing time compared to the APSO algorithm. However, this study only focuses on the preliminary exploration of enterprise financial data processing. In the future, algorithm application scenarios can be further expanded to optimize system functions to meet more complex business needs.
Hongyan Zhang (Tue,) studied this question.