Evaluation method for public guidance service interface design based on improved grey H-Convex correlation model: integrating layout aesthetics and user visual cognition

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.