However, comprehensive measures of the impact of personal information, such as basic information, physiological indicators, psychological indicators, and indoor and outdoor physical parameters, which affect individual comfort indicators, on thermal comfort are still lacking. (2) Consideration of material factors. Materials affect indoor environment comfort, which has been confirmed in many papers [40]. For example, [PERSON]. [41] hypothesized that the introduction of nature into the built environment can improve users' sense of well-being, similar to that in the wooden environment. Refs. [42; 43] Studies have shown that the difference in the proportion of wood in the interior wall can lead to different physiological responses of the occupants. It showed that surgical recovery patients have different needs for pain medication in wooded versus non-wooded environments in health care facilities [44; 45]. It proposed that in the classroom environment, wood plays a positive role in improving attention and reducing stress [46]. However, the effect of this material on thermal comfort has not been mentioned in the existing thermal comfort models. Therefore, it is very important to construct an appropriate model to study the effect of different materials on thermal comfort. (3) Design of the machine learning algorithm. At present, the algorithms used in thermal comfort model research can be roughly divided into random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbor (KNN), gradient boosting machine (GBM), decision tree (DT) algorithms, and other algorithms [47; 48; 49]. Although these algorithms can obtain good performance in the learning process, they also have the following limitations: (1) they easily fall into local minima, which affects the optimal learning speed; and (2) it is difficult for the constructed model to have good interpretability. Therefore, the following factors are considered in this study: (1) Existing thermal comfort models are based on the view of a single material. However, how to convert a single view to an observational view based on two or more materials is explored. What is the effect of the influence of multiple materials on human thermal comfort? (2) Unlike the single view decision-making methods, how can we consider the impact of two or more different types of materials on human thermal comfort to construct a thermal comfort model? (3) The black-box phenomenon of the traditional neural network algorithm makes it difficult to explain the meaning of the output of the middle layer and the output of the system. How to construct a fuzzy classifier with good performance and high interpretability is also one of the difficulties in this study. Therefore, the research contribution of this study can be summarized as follows:  
(1) Collection of datasets. To obtain relatively large and comprehensive training samples and avoid differences in the thermal expectations of individuals of different ages and climate zones, subjects of a specific age were selected for laboratory situations using two materials in a given climate zone (the humid climate zone that is south of the north subtropical zone is between 31\({}^{\circ}\)43\({}^{\prime}\)12\({}^{\prime\prime}\)-32\({}^{\circ}\)02\({}^{\prime\prime}\) N and 120\({}^{\circ}\)21\({}^{\prime}\)57\({}^{\prime\prime}\)-120\({}^{\circ}\)52\({}^{\prime\prime}\) E) to measure the indoor physical environment and the physiological and psychological indicators of the human body (see Section 4.1 for details). (2) Model construction. The joint membership function from two views was constructed to take into account the effect of the characteristic information of different materials on the thermal comfort of the human body. Additionally, the design of the feature mapping space under the joint view not only preserves the feature information of the training sample from a single view but also considers the impact of different materials on the individual's thermal comfort, thereby optimizing the entire training model.