研究表明，约41.6%的中国网民通过网络搜索医疗健康知识，但虚假、劣质中文健康信息充斥网络，质量参差不齐。如何提升消费者对网络健康信息有效质量评估结果的信任，是新学科“消费者健康信息学”的最新研究焦点。本研究从消费者对网络健康信息的信任演化出发，通过对该过程中，语境、信任和信息质量作用关系的处理、分析和推理，克服信任的主观性、复杂性、动态性和不确定性所造成的影响，形成较完整的可执行演化机理，建立信息质量综合评估体系。首先，课题基于TURF易用性框架和监督学习技术，通过本体方法，有效描述消费者对健康信息信任的动态演化过程。其次，通过贝叶斯网络，自适应地评估信任演化过程中的信息质量。最后，采用基于图方法的质量可视化和基于眼动轨迹的分析，验证所提出信任演化机制的有效性。在此基础上，研发操作性强、动态自适应的信任演化建模方法，建立信息质量综合评估理论与技术体系，为“把健康融入所有政策”保驾护航。

About 41.6% of Chinese Internet users reportedly search on the Internet for medical health information, but false or inferior health information in Chinese prevails in the Internet. Thus, how to improve consumer trust in the valid results of online health information quality evaluation becomes the latest research hotspot of the new discipline -- "consumer health informatics". This study was targeted at the evolution of consumer trust in online health information. Specifically, the interactive relationship among contexts, trust and information quality was processed, analyzed and inferred, and the effects of subjectivity, complexity, dynamics and uncertainty on consumer trust were eliminated. Thereby, a complete executable evolution mechanism and an information quality comprehensive evaluation system were established. Firstly, based on TURF usability framework and supervised learning, the dynamic evolution of health information customer trust was successfully described by using the ontology method. Secondly, the information quality of trust evolution was adaptively evaluated via Bayes networks. Finally, the effectiveness of the trust evolution mechanism was validated through graph-based quality visualization and analysis of eye movement orbits. Thereby, a trust evolution modeling method with operability and dynamic adaptiveness was developed, and a theoretical and technical system of information quality comprehensive evaluation was established, which will underlie and help with "the integration of health into all policies".