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.

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.

Film posters serve as front-end visual communication media that shape viewers’ initial judgments of film genre, emotional tone, and viewing appeal. However, whether the optimal overall color configuration follows a universal rule or varies across poster types remains insufficiently examined. This study investigated how overall lightness and chroma influence the communication effects of film posters and identified type-specific optimal color intervals. Based on a cross-type poster sample library, film posters were classified into four visual grammar types: affable-entertaining, relational-emotional, spectacle-dynamic, and threat-suspenseful. Type-specific quantile thresholds for lightness and chroma were established within each category. Eye-tracking data, subjective ratings, mixed-effects response surface modeling, and constrained desirability optimization were combined to identify optimal regions of overall color configuration. The results show that no single optimal lightness–chroma interval applies across all poster types. The dominant optimal interval was low lightness–high chroma for affable-entertaining and relational-emotional posters, high lightness–low chroma for spectacle-dynamic posters, and medium lightness–high chroma for threat-suspenseful posters. These findings indicate that overall color optimization varies across poster types within the present experimental context and provide practical support for evidence-based, type-specific poster color design.

Driving anxiety is a major issue that compromises the safety and experience of driving. It has been demonstrated that negative emotions with a high arousal factor like anxiety are manifested in body posture and sitting behavior. This paper will investigate one of the ways of identifying anxiety by assessing the pressure distribution in the sitting posture, and discuss driving situations that have a strong correlation with causing anxiety. Thirty people were recruited through a campus social media platform. The experimental design was a one-factor within-subject experimental design in which the researcher used standardized audio materials and a digital countdown task as a means (or inducement) of achieving calm (baseline) and anxiety, respectively. The induction effects were validated using the Self-Assessment Measure (SAM). Also, a pathway accommodating eight driving conditions was established to address the depth of pressure distribution in each condition by means of pressure mats to examine the behavior of the subjects in the relaxed and anxious conditions. The evaluation of both subjective and objective data was performed using the Wilcoxon signed-rank test, and at the same time, we explored the relationships that existed among the driving situations and anxiety levels. The research findings reveal the following: (1) Compared to baseline emotional state, anxiety-induced conditions exhibit heightened pressure distribution and increased volatility in the thigh, hip, and lower back regions, accompanied by greater anterior–posterior center-of-gravity sway. (2) The study identified 40 significant features distinguishing anxiety from calmness, including aTHR_Max and rCOPBTL_Std, primarily distributed across the left leg, right hip, and lower back regions. (3) Through baseline correction and cosine similarity analysis, scenarios prone to triggering anxiety were identified as those involving high uncertainty and high interactivity (e.g., traffic congestion and entering roundabouts); scenarios characterized by continuity and high predictability (e.g., consecutive turns and parking) showed weaker associations with anxiety. This study provides new data support and design rationale for in-vehicle emotion recognition systems and emotion-intervention-based human–machine interaction design.