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

ABSTRACT:

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 p

SUBMITTER: Chen S 

PROVIDER: S-EPMC12271214 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature