互联网+医疗健康是我国长期战略，且在新冠肺炎疫情期间为患者就诊提供了新渠道。但就诊决策高风险引发患者信任危机，成为其发展的瓶颈问题。患者以在线医疗app中其他患者的问诊和反馈作为依据建立间接感知信任，其本质是信任传递过程。患者感知信任具有模糊性、多因素性、多源性以及负面信息的高敏感性，且信任传递链具有高密度短线程特点。本项目针对在线医疗背景下高密度短线程传递链上的多源模糊感知信任传递问题进行研究，提出“先转化，后聚合，再融合”的思路，构建基于直觉模糊集的互联网医疗服务感知信任传递模型。将直觉模糊集多属性决策方法与评价偏离度结合，实现主观感知信任的转化；引入前景理论构建模糊推理规则，考虑从众心理和锚定效应构建冲突信任聚合方法；结合基于案例的分析方法构建考虑经验的多源信任融合模型。突破传统信任传递模型难以融入信任影响因素和病症属性，更缺乏处理冲突和多源信任机制的局限，推动在线医疗服务迅速发展。

As a supplement in Public health during COVID-19 epidemic, Internet Plus healthcare service plays a vital role in primary screening COVID-19, prevention of cross infection and relief of pressure on public hospital. Meanwhile, Internet Plus healthcare service is also a long-term important measure taken by our government in recent years to alleviate the uneven distribution of high-quality medical resources. Under the guidance of many national policies, Internet Plus healthcare service has developed rapidly in recent years. But up to now, it is only widely used in online hospital registration. The high risk of diagnosis and treatment decision-making leads to a serious crisis of patients' trust in online medical services, which is a bottleneck problem restricting the development and promotion of Internet Plus healthcare service. Therefore, how to establish patient's perceived trust effectively is the main difficulty in the development of Internet Plus healthcare service..Based on the recommendation information of other patients in the online medical APP, such as consultation information, diagnosis results and treatment effect feedback, patients establish their perceived trust, which is a typical trust transitivity process in essence. Relevant research shows that trust transitivity is an effective way to establish online perceived trust quickly. However, the specialty of the perception and trust transitivity process in the Internet Plus healthcare service brings new challenges to the trust transitivity model: (1)The characteristics of perceived trust, which is high subjectivity, fuzziness and multi-sources, and the perceived recommendation trust has the symptom similarity dependence, bring difficulties to representation and calculation of perceived recommendation trust. (2) The perceived high risk of online medical services leads to patients' high sensitivity to negative information, which makes it difficult to consensus negative and positive perceived recommendation trust. (3) The obvious multi-sources characteristics of patients’ perceived trust, which originated in others’ recommendation information and self-experience, brings about the problem of multi-sources trust fusion. .Based on the deep analysis of the multi-staged courses and multi-sources of perceived trust, we put forward research rout of “trust transformation - trust consensus - trust fusion” to establish our model. The model is established on the Intuitionistic Fuzzy Set Multi-attribute Decision-making Method and integrates the disease characteristics and behavior factors of patients, which is called trust transitivity model in Internet Plus healthcare based on Intuitionistic Fuzzy Set. First, recommendation information has been transformed into differentiated perceived trust by combining intuitionistic fuzzy set multi-attribute decision-making method with evaluation deviation in order to reflect multi-attribute and multi-stage characteristics of the disease. Second, Prospect Theory has been used to design reasoning rules to establish conflict trust consensus method according to Herd Behavior and Anchoring Effect. Third, recommendation trust and experience trust has been integrated into multi-sources trust fusion method, which has been proposed by combing Case-based Decision-making Theory and Consistency Theory. .In this project, traditional trust transitivity model has been expanded in both frame structure and intension by integrating various methods. This model can not only improve the theoretical system, but also has significance for interdisciplinary research. It can be used as the algorithm kernel to help the Internet Plus healthcare service platform predict perceived trust of different patients to specific online medical services, recommend effective information according to patients' illness, help patients quickly establish their perception trust. Thus, this model finally promotes the rapid development of the Internet Plus healthcare service.