Research on Credit Evaluation Model of Railway Supervision Enterprises Based on Fuzzy Comprehensive Evaluation Method

To address issues such as an imperfect indicator system, difficulty in determining indicator weights, and the inability of credit ratings to reflect the true credit levels of railway supervision enterprises, this study constructs an evaluation indicator system based on three main factors affecting the credit levels of supervision enterprises: corporate quality, supervision management, and reward-penalty records. Both subjective and objective factors are considered, and a combined weighting method using the Analytic Hierarchy Process (AHP) and Entropy Method is employed to determine the weights of evaluation indicators. Based on the Fuzzy Comprehensive Evaluation, the credit scores of enterprises are calculated. Additionally, Grey Relational Analysis (GRA) is used to determine the membership degree of evaluation indicators for credit ratings, thereby forming a credit evaluation model for railway supervision enterprises. To validate the effectiveness of the model, the credit scores of railway supervision enterprises are calculated based on actual data, demonstrating that the proposed model can provide a reasonable evaluation of the credit levels of railway supervision enterprises.