To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph construction based on graph attention neural networks (GAT). Using Tang Dynasty gold and silver artifacts as samples, we establish a joint knowledge extraction model based on GAT. The model employs the BERT pretraining model to encode collected textual knowledge data, conducts sentence dependency analysis, and utilizes GAT to allocate weights among entities, thereby enhancing the identification of target entities and their relationships. Comparative experiments on public datasets demonstrate that this model significantly outperforms baseline models in extraction effectiveness. Finally, the proposed method is applied to the construction of a knowledge graph for Tang Dynasty gold and silver artifacts. Taking the Gilded Musician Pattern Silver Cup as an example, this method provides designers with a visualized and interconnected knowledge collection structure.

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.

To address current product styling design issues, such as ignoring the joint effects of multiple styling elements when constructing perceptual imagery fitting models and thus failing to effectively identify the relationships between styling elements, a product styling design method based on fuzzy set qualitative comparative analysis (fsQCA) is proposed. This method first uses semantic differential and statistical methods to obtain users’ evaluative vocabulary for the product’s perceptual imagery. Then, morphological analysis and cluster analysis are employed to establish typical product samples and extract styling elements to create a styling feature library. Perceptual imagery ratings of these styling features are obtained through expert evaluation. fsQCA is then used to analyze the different grouping relationships between styling elements and their influence on product styling imagery, aiming to match user intentions through different element combination paths. The results show that this method achieves a consistency value of 0.9 for the most optimal styling configurations, demonstrating that fsQCA can effectively identify the multiple paths of product styling elements that meet users’ needs. The contributions of this study to the related fields are: (1) providing a new perspective on the relationship between user perceptual imagery and predicted product styling elements, and (2) advancing the theoretical basis for studying multiple paths of product styling elements. The research results demonstrate that using the fsQCA-based product styling design method can accurately portray the multiple paths of product styling elements that meet users’ needs, thereby effectively improving design efficiency. Finally, a teapot styling design study is used as an example to further verify the method’s feasibility.

The development of scientifically rigorous evaluation methods is essential to overcome three persistent challenges in public navigation interfaces: inadequate guidance, low usability, and suboptimal user experience. Focusing on intelligent medical guidance systems, this study establishes a dual-dimensional analytical framework encompassing layout aesthetics (spatial composition principles) and visual cognition (information processing patterns). We propose an enhanced grey H-convex correlation model integrating Bayesian Best Worst Method (BBWM) and modified CRITIC with reference point (M-CRITIC-RP) to address weight determination limitations in existing models. Our experimental analysis reveals two key findings: First, the synergistic integration of layout aesthetics (e.g., visual hierarchy balance) and visual cognition characteristics (e.g., attention distribution patterns) significantly improves interface usability for medical service navigation. Second, the proposed BBWM-M-CRITIC-RP hybrid model demonstrates superior performance in quantifying aesthetic-cognition relationships, achieving 88% prediction accuracy compared to conventional methods. In a word, our research provides a new theoretical method for traditional visual display design and a new evaluation criterion for interface design, aiming at improving the user experience.

With the development of smart technology and the increasing variety of everyday products, factors influencing product service touchpoint design have become more diverse and complex. Existing service touchpoint design methods and models often focus narrowly on user research, co-design, and risk analyses, lacking a systematic approach. Consequently, they struggle to deliver solutions that align with user needs. This misalignment may result in issues such as increased cognitive load during product use, a diminished user experience, and lower evaluations of the product. In response, this paper proposes a service touchpoint design model, the “BEDFITA” model. It starts with user behavior and follows a structured, systematic process that includes understanding user behavior, recording user emotions, matching user needs, designing product functions, planning interaction experiences, designing service touchpoints, and analyzing failure risks. The Kano model is employed in the user requirement identification phase to provide more precise user requirement parameters, while FMEA is employed in the failure risk analysis phase to generate more accurate failure risk assessments. This ensures that the final service touchpoint design meets user needs and offers reliability and robustness. Finally, the feasibility and effectiveness of the proposed model are validated through a case study on the service touchpoint design of a smart desk.