Consumer behavioral decision-making process on the digital economy platform is complex and fuzzy, and traditional models are difficult to accurately reflect irrational consumer decision-making characteristics. This paper proposes a consumer behavior decision modeling method based on fuzzy logic and prospect theory to address the complexity of the consumer behavior decision-making process on the digital economy platform. Firstly, a consumer three-dimensional portrait system is constructed, and the consumer behavior data is analyzed in multiple dimensions by FCM clustering algorithm; secondly, a consumer multi-attribute behavioral decision-making model is constructed based on prospect theory, and an intuitive triangular fuzzy number is introduced for optimization, to overcome the limitations of the traditional model in dealing with the fuzzy decision-making information; lastly, a B2C e-commerce service whole-process evaluation index system is constructed by using the service blueprint method, and an evaluation index system for B2C e-commerce service whole-process evaluation is built through LDA topic extraction and LSTM sentiment analysis techniques to mine consumer behavior attributes and preferences. The experimental results show that the optimized model has an accuracy of 84.59% in the test of 120 commodity samples, which is 2.63% higher than that of the unoptimized model; the stability of the model prediction improves significantly with the increase of the sample size to 480 items. Tests based on 12 commodities show that the model predicted sorting matches the actual sales sorting well, with only 2 commodities not matching the sorting. This study provides new ideas for the analysis and prediction of consumer behavior on digital economy platforms, and has practical value for service quality evaluation and decision optimization of e-commerce enterprises.
