Exploring the relationship between audio–visual perception in Fuzhou universities and college students' attention restoration quality using machine learning

Objective: In response to the challenges posed by mental health issues among college students and the declining quality of campus environments, this study aims to reveal the complex mechanisms underlying the relationship between campus audiovisual environments and the quality of students' attention recovery. It further explores campus landscape optimization pathways driven by multi-source data, providing scientific basis for sustainable campus planning. Methods: Taking Fuzhou University Town as a case study, this study integrates machine learning technology with multi-source data (street view images, social media text, and PRS-11 questionnaires) to construct a "multi-modal perception mechanism analysis-dynamic evaluation iteration" framework. The CNN-BiLSTM model was used to predict attention recovery quality, combined with HRNet semantic segmentation, GBRT soundscape prediction, and CSV-T4SA sentiment analysis models to quantify audiovisual elements. XGBoost models and SHAP interpretability analysis were employed to reveal the effects and interaction mechanisms of variables. Results: (1) Attention recovery quality is significantly higher in liberal arts and agricultural/forestry universities than in science and engineering universities, with boundary effects and the synergistic design of humanistic soundscapes being key factors; (2) SHAP analysis identifies humanistic soundscapes, natural soundscapes, and color complexity as core influencing factors, with their effects exhibiting significant threshold characteristics; (3) Linear interaction mechanisms among audiovisual elements are discovered, such as the interaction between vegetation density and building enclosure degree enhancing recovery efficacy, and the synergistic design of musical soundscapes and paving materials can optimize perceptual experiences. Conclusion: By innovatively integrating multi-source data and machine learning techniques, this study systematically analyzes the relationship between campus audiovisual environments and attention recovery, breaking through the limitations of traditional linear analysis. The proposed "threshold response design" and "cross-modal collaborative optimization" strategies provide a new paradigm for campus planning, validate the scientific value of multi-sensory interaction design for mental health promotion, and offer a transferable methodological framework for global university environmental upgrades.