Construction of fuzzy mathematical model for judging financial indexes of private enterprises under the perspective of deep learning

Abstract

This paper introduces a novel financial evaluation framework combining deep learning, hierarchical analysis, and fuzzy comprehensive evaluation to form the AHP-fuzzy comprehensive model. This model is designed to refine financial analysis by constructing precise evaluation indexes and determining weight values, specifically tailored for the financial management of private enterprises. Through a case study on Enterprise A, focusing on solvency, operational efficiency, and cash flow, we observed significant trends: a decline in the quick ratio from 0.92 in 2017 to 0.49 in 2021, a decrease in the accounts payable turnover ratio from 2.58 in 2017 to 2.01 in 2020, and a concerning downward trend in the cash to current liabilities ratio, culminating in −11.30% in 2020. These findings validate the effectiveness of the AHP-fuzzy comprehensive evaluation model in providing nuanced financial assessments for private enterprises.