User satisfaction serves as a crucial reference for iteratively optimizing software interface designs. This paper introduces a comprehensive measurement model of user satisfaction, employing Notability and Goodnotes for case studies. The proposed model incorporates facial recognition technology to gauge the intensity of users’ facial expressions while interacting with various functions of the target interface. Additionally, an experimental observation method is employed to gather objective data, including task completion time, task success rates, and operational procedures, alongside users’ subjective evaluations. Leveraging objective data as input and subjective ratings as output, a user satisfaction prediction model based on a PSO-BP neural network has been devised. The results demonstrate an impressive accuracy rate of 86.26%, indicating a high accuracy in subjective perception prediction. This model has proven to be effective for measuring user satisfaction and evaluating software interface usability. Moreover, this research contributes to expanding the repertoire of user interface satisfaction evaluation methods, enhancing the objectivity of measurements and surpassing the efficiency of conventional experimental evaluation techniques. The proposed model holds practical significance for software interface usability assessment and optimization design.

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots.

As digital heritage platforms become increasingly immersive and information-rich, digital enrichment may offer experiential benefits while also being associated with greater cognitive demands. Drawing on Cognitive Load Theory and an intertemporal perspective, this study develops a cognitive–intertemporal framework linking perceived digital complexity, perceived spatial narrative richness, cognitive load, temporal discounting tendency, and sustainable decision-making intention. Using Digital Dunhuang as the empirical context, a cross-sectional survey of 603 users recruited through non-probability social media sampling was analyzed using structural equation modeling and bias-corrected bootstrapping. The results showed that perceived digital complexity and perceived spatial narrative richness were positively associated with cognitive load, which was positively associated with temporal discounting tendency, while temporal discounting tendency was negatively associated with sustainable decision-making intention. Although cognitive load was negatively associated with sustainable decision-making intention in the hypothesized model, this direct association became nonsignificant in the better-fitting extended partial-mediation model. The sequential indirect associations remained significant. Significant sequential indirect associations with sustainable decision-making intention were observed for both perceived digital complexity through cognitive load and temporal discounting tendency and perceived spatial narrative richness through cognitive load and temporal discounting tendency. Digital literacy attenuated the associations of perceived digital complexity and perceived spatial narrative richness with cognitive load, whereas future self-continuity attenuated the association between cognitive load and temporal discounting tendency but not that between cognitive load and sustainable decision-making intention. These findings identify a theoretically coherent cognitive–intertemporal pattern linking perceived digital heritage characteristics with sustainability-oriented intention, extending digital heritage research while underscoring the need for longitudinal or experimental evidence to establish temporal and causal ordering.