Enterprise Economic Benefit Evaluation Model Based on Fuzzy Entropy

This paper proposes a new evaluation model based on fuzzy entropy to address the accuracy and efficiency issues of traditional methods for evaluating the economic benefits of enterprises. This method utilizes carefully designed production functions and intuitive complementary judgment matrices to comprehensively reflect multiple dimensions of the economic benefits of the enterprise. To more accurately quantify these dimensions, this method transforms the intuitive judgment matrix into intuitive fuzzy numbers and constructs an evaluation model using fuzzy entropy. By using hierarchical weight vectors, the weights of various indicators are scientifically calculated to improve the accuracy of evaluation and make it more objective and comprehensive. Meanwhile, this method also scores the economic efficiency level of the enterprise through hierarchical settings, making the evaluation results more intuitive and facilitating the formulation of improvement strategies by the enterprise. The experiment uses advanced technology and algorithms to verify the effectiveness of the model. The results show that the accuracy of the new method is as high as 96.0%, far exceeding traditional methods, and the evaluation is completed in only 5.2 s, greatly improving the evaluation efficiency and saving time and resources for enterprises. Compared with traditional methods, the new method significantly improves accuracy and evaluation efficiency, demonstrating the effectiveness of fuzzy entropy in enterprise economic benefit evaluation and demonstrating the superiority of the new method in dealing with complex economic evaluation problems. This method is efficient and accurate, bringing breakthroughs to the evaluation of enterprise economic benefits, and has extremely high practical value.