This study aims to explore the application and effect of affective computing technology in consumer experience evaluation and satisfaction prediction. Through in-depth analysis of consumer reviews on online platforms, this study uses natural language processing technology to extract emotional features and builds an affective computing model to evaluate consumers’ emotional tendencies. The study discovered a strong positive correlation between emotional propensity scores and consumer satisfaction. A satisfaction prediction model using these emotional characteristics showed good predictive performance. Despite the positive results of the study, several limitations were identified, including the adaptability of the model and the challenges of processing complex text expressions. Future research will explore ways to improve affective computing models, introduce more dimensions of data, and develop cross-cultural and cross-language models, aiming to improve the accuracy and generalization capabilities of the models and provide enterprises with deeper consumer insights and strategies to optimize customer experience.