The appearance characteristics of ceramic color are an important factor in determining the user’s aesthetic perception of the product. Given the problem that ceramic color varies and the user’s visual sensory evaluation of color is highly subjective and uncertain, a method of quantifying ceramic color characteristics based on the Back Propagation (BP) neural network algorithm is proposed. The semantic difference method and statistical method were used to obtain quantified data from ceramic color perceptual semantic features and were combined with a neural network to study the association between ceramic color features and user perceptual-cognitive evaluation. A BP neural network was used to build a ceramic color perceptual semantic mapping model, using color semantic quantified values as the input layer, color L, A, and B component values as the output layer, and model training to predict the sample. The output color L, A, and B components are used as the input layer and the color scheme was designed. The above method can effectively solve the mapping problem between the appearance characteristics of ceramic color and perceptual semantics and provide a decision basis for ceramic product color design. The case application of color design of daily-use ceramic products was conducted to verify the effectiveness and feasibility of the quantitative research method of ceramic color imagery.

Modeling is the primary point to conveying product characteristics. Accurately capturing the image needs of users for modeling is an important way to effectively improve product design efficiency. Quantitative theory type I is a method to solve the internal law between the user’s target image and the product modeling characteristics by building a mathematical model. Aiming at the problems of abundant product modeling design elements, diversified combination methods, and high design cost due to the subjective ambiguity of user images, a topological design method of product modeling based on quantification theory I is proposed. This method uses the semantic difference method and statistical method to obtain the quantitative data of perceptual semantic features of product modeling to represent the explicit and implicit needs of users. Based on the topological transformation, the topological analysis and modeling of the two types of requirements are carried out. The mapping model of product modeling features and style images is constructed using quantitative theory type I, and the topologic value of each modeling design element is calculated. The above method can effectively solve the mapping problem between product modeling features and style images. On this basis, this research provides a decision-making basis for product modeling design. Using a wine jug as a product exemplar for this research, the scheme design and evaluation are carried out to verify the effectiveness and feasibility of the product modeling topological design method.

In the promotion of sustainable modes of transport, especially public transport, reasonable failure risk assessment at the critical moment in the process of service provider touch with users can improve the service quality to a certain extent. This study presents a product service touch point evaluation approach based on the importance–performance analysis (IPA) of user and failure mode and effect analysis (FMEA). Firstly, the authors capture service product service touch points in the process of user interaction with the product by observing the user behavior in a speculative design experiment, and perform the correlation analysis of the service product service touch point. Second, the authors use the IPA analysis method to evaluate and classify the product service touch points and identify the key product service touch points. Thirdly, the authors propose to analyze the failure of key product service touch points based on user-perceived affective interaction and clarify the priority of each key touch point. Finally, reluctant interpersonal communication, as the key failure caused by high risk, is derived according to the evaluation report, which leads to establishing new product service touch points and improving the overall user experience to promote sustainable transports with similar forms and characteristics.

Built environment elements management involving heritage buildings requires a nuanced approach that balances cultural preservation, planning efficiency, and resource optimization. Conventional evaluation methods frequently neglect public perception, leading to misaligned priorities and ineffective heritage resource deployment. To address this gap, this study proposes a Sandglass Tiered Model that integrates public perception into the value assessment process of culturally significant buildings. By integrating multi-source perception data and cultural ontology through a structured, tiered data collection mechanism, the model translates subjective views into four quantifiable indices: symbolizability, authenticity, readability, and regionality. These indices form the basis of an AHP-GRA–driven assessment framework, facilitating value-based prioritization and spatial zoning of heritage elements within construction projects. The model was empirically validated in the Yiling Cultural Heritage Area, where it effectively facilitated differentiated building strategies, optimized resource sequencing, and improved alignment between project goals and stakeholder expectations. Importantly, the model provides a transferable framework that embeds cultural awareness into the lifecycle of heritage building projects—from pre-design evaluation to renovation and adaptive reuse. By integrating public perception into construction workflows, this approach provides a dynamic and participatory framework for managing complex heritage assets within urban development contexts. It improves the precision, responsiveness, and cultural sensitivity of construction planning, offering practical insights for policymakers, architects, and construction managers working in resource-intensive or culturally rich environments.

During the cloud-based product form design process, traditional collaborative-filtering recommendation methods fail to effectively calculate similarity metrics or generate relevant knowledge recommendations for newly joined designers, due to their lack of historical knowledge scores, resulting in inefficient knowledge acquisition. Since designers show a clear tendency of professional trust in the knowledge adoption process, they are more inclined to accept knowledge resources recommended by people with similar professional backgrounds to theirs or by authorities in their fields. Therefore, this paper proposes a knowledge recommendation method for product form design integrating crowd-intelligence context similarity and trust relationships in cloud environments. The method first constructs an ontology model and a product form design knowledge ontology, containing task context, designer’s context, and computational context to facilitate the acquisition, storage, processing, and invocation of contextual information and knowledge. Second, the neighboring set of target designers is determined by calculating the multidimensional contextual similarity and trust relationship between designers. Finally, the missing knowledge score of the target designer is predicted by the knowledge evaluation of the neighboring designers, and the recommendation list is generated. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design.