Fig. 1: The proposed research framework.

This figure presents the proposed research framework for optimizing the Panoramic Virtual Museum Interactive Interface (PVMUI). The framework consists of three phases: Phase 1: Image Collection and Factor Analysis Extraction of Kansei Words • PVMUI images are gathered from various online sources. • Kansei words are collected through online questionnaires and screened. • The KJ Method is used to filter Kansei words, followed by Factor Analysis (FA) Clustering to extract core emotional dimensions (Technological, Innovative, and Fancy). Phase 2: Entropy Weight-TOPSIS Morphological Deconstruction and Extraction. • The design features of PVMUI are deconstructed. • The Entropy Weight-TOPSIS method is applied to identify and extract critical interface design features based on user perception. Phase 3: PSO-SVR Mapping of Kansei Words and Design Features. • Particle Swarm Optimization (PSO) is used to optimize Support Vector Regression (SVR) parameters. • The SVR model is then used to predict the mapping between user emotions and design features, refining the optimal PVMUI configuration.