As universities become increasingly open, campuses are no longer only places for study and daily life for students and faculty, but also essential spaces for public visits and cultural identity. Traditional perception evaluation methods that rely on manual surveys are limited by sample size and subjective bias, making it challenging to reveal differences in experiences between groups (students/visitors) and the complex relationships between spatial elements and perceptions. This study uses a comprehensive open university in China as a case study to address this. It proposes a research framework that combines street-view image semantic segmentation, perception survey scores, and interpretable machine learning with sample augmentation. First, full-sample modeling is used to identify key image semantic features influencing perception indicators (nature, culture, aesthetics), and then to compare how students and visitors differ in their perceptions and preferences across campus spaces. To overcome the imbalance in survey data caused by group–space interactions, the study applies the CTGAN method, which expands minority samples through conditional generation while preserving distribution authenticity, thereby improving the robustness and interpretability of the model. Based on this, attribution analysis with an interpretable decision tree algorithm further quantifies semantic features’ contribution, direction, and thresholds to perceptions, uncovering heterogeneity in perception mechanisms across groups. The results provide methodological support for perception evaluation of campus functional zones and offer data-driven, human-centered references for campus planning and design optimization.
Zhuang et al. (Thu,) studied this question.