Construction of Evaluation Index System and Optimization Algorithm for Accounting Big Data Capabilities
Year: 2024
Type: conference-paper
Abstract: Within the wide application of big data, it is essential for accounting professionals to possess both a robust foundation in accounting principles and the skills necessary for handling and analyzing large datasets. This study proposes a systematic framework for assessing big data capabilities in accounting and explores advanced optimization algorithms to improve evaluation accuracy and effectiveness. The study designed a specific evaluation index system based on four dimensions: professional knowledge and skills, communication and coordination, professional ethics and integrity, and continuous learning and innovation. Using the Analytic Hierarchy Process (AHP) to determine the weights, it combined the fuzzy comprehensive evaluation method for quantitative assessment. In addition, a machine learning-based random forest algorithm is introduced to optimize the evaluation process by combining feature selection and dimension reduction technology. Through empirical research, 10 representative enterprises in different industries are selected for verification. The results show that the evaluation system and optimization algorithm can effectively reflect big data capabilities of accountants, and the random forest algorithm is better than the conventional method in running time and accuracy.
Authors: Lu Wang, Changping Jia
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4412524705