人工智能（Artificial Intelligence，AI）算法正在重塑在线旅游平台的价格形成机制，但消费者对 AI 动态定价的反应并非仅取决于价格水平，还受到定价过程透明度、个人数据使用边界以及平台动机判断的共同影响。本文基于刺激—机体—反应（Stimulus-Organism-Response，SOR）框架、认知评价理论与公平理论，构建“AI 动态定价特征—感知公平性—平台信任—支付意愿”的链式机制模型，并基于 696 份江苏居民问卷数据进行结构方程模型检验，辅以随机森林预测分析与探索性聚类分析。结果表明：感知透明度对支付意愿具有显著正向直接影响，感知侵入性对支付意愿具有显著负向直接影响，而个性化感知的直接效应不显著；感知公平性与平台信任在感知透明度、感知侵入性影响支付意愿的过程中发挥显著链式中介作用，但在个性化感知路径中未表现出显著中介效应；感知公平性对支付意愿的直接效应（β=0.249）大于其经由平台信任产生的间接效应（β=0.131），说明公平认知既能直接影响消费者支付意愿，也能通过增强平台信任产生间接影响。研究结果表明，AI 动态定价情境下消费者支付意愿的形成并非单一的信任传导过程，而是公平认知直接作用与“公平认知—平台信任”间接传导并存的双重机制。基于此，本文提出提升定价透明度、降低侵入性感知、实施差异化沟通等策略建议，为在线旅游平台优化 AI 定价策略与提升消费者信任提供理论参考。

Artificial intelligence (AI) algorithms are reshaping price formation mechanisms on online travel platforms. However, consumers’ responses to AI-based dynamic pricing are not determined solely by price levels; they are also jointly influenced by the transparency of pricing processes, the perceived boundaries of personal data use, and judgments regarding platform motives. Drawing on the Stimulus–Organism–Response (SOR) framework, cognitive appraisal theory, and fairness theory, this study develops a serial mechanism model linking AI dynamic pricing characteristics, perceived fairness, platform trust, and willingness to pay. Based on survey data from 696 residents in Jiangsu Province, the proposed model is tested using structural equation modeling, supplemented by random forest prediction analysis and exploratory clustering analysis. The results show that perceived transparency has a significant positive direct effect on willingness to pay, whereas perceived intrusiveness has a significant negative direct effect; by contrast, the direct effect of perceived personalization is not significant. Perceived fairness and platform trust play a significant serial mediating role in the effects of perceived transparency and perceived intrusiveness on willingness to pay, but no significant mediating effect is observed in the perceived personalization path. Moreover, the direct effect of perceived fairness on willingness to pay (β = 0.249) is greater than its indirect effect through platform trust (β = 0.131), indicating that fairness cognition can not only directly shape consumers’ willingness to pay but also exert an indirect influence by enhancing platform trust. These findings suggest that, in the context of AI dynamic pricing, consumers’ willingness to pay is not formed through a single trust-transmission process; rather, it reflects a dual mechanism in which the direct effect of fairness cognition coexists with the indirect transmission pathway of “fairness cognition–platform trust.” Accordingly, this study proposes practical strategies such as improving pricing transparency, reducing perceived intrusiveness, and implementing differentiated communication, thereby providing theoretical implications for online travel platforms to optimize AI pricing strategies and enhance consumer trust.