Evaluation and Enhancement of Financial Accounting Rpa Based on Artificial Intelligence: A Text Mining and McDm Study

The integration of automation and intelligence in financial accounting has become increasingly profound, with their scope and significance continually expanding. However, there are notable differences in their functional focus: automation primarily aims to enhance the efficiency of accounting data processing, whereas intelligence places greater emphasis on optimizing the accuracy and effectiveness of data handling. This study proposes an innovative two-stage evaluation process designed to systematically analyze the impact and developmental trajectory of robotic process automation (RPA) in financial accounting. The research begins by applying text mining techniques to extract key insights from extensive literature and practical data. Subsequently, two multi-criteria decision-making (MCDM) methods are employed to conduct comprehensive evaluations and prioritize influential factors, thereby identifying their relative importance. The final evaluation framework comprises five major dimensions and twelve assessment criteria, which are interrelated and critically important for decision-making processes. The findings demonstrate that implementing RPA can significantly enhance operational efficiency and effectively optimize resource allocation, leading to improved organizational performance. Furthermore, the analysis suggests that by advancing intelligent accounting, intelligent auditing, and intelligent taxation, the financial accounting profession can align with overarching strategic goals in financial information decision-making, technological development, and practical application, thereby achieving optimal outcomes.