The swift advancement of e-commerce in agricultural products has heightened credit risks, making the establishment of a novel credit evaluation system in the big data era a matter of urgency. Utilizing the B2C model, the credit evaluation process is facilitated through an enhanced fuzzy comprehensive evaluation approach, which is preceded by the development of an e-commerce credit evaluation index system tailored for agricultural products. This innovative methodology takes into account the real-time, dynamic, and fuzzy attributes of the indices within the big data environment, integrating both subjective and objective empowerment methods. This not only leverages human intelligence but also delves into the inherent characteristics of the data. The triangular fuzzy analytic hierarchy process is employed to address the inherent fuzziness in human reasoning, while the principle of maximum membership is modified to tackle the issue of information loss. To validate the effectiveness of this new approach, actual evaluations were conducted on agricultural product e-commerce samples from Tmall.