Resumen
As the university campus is a place for learning, conducting scientific research, and communication, campus street spatial quality has an impact on its users. Therefore, refinement evaluations of campus spatial quality are essential for constructing high-quality campuses. In this study, machine learning was used to conduct semantic segmentation and spatial perception prediction on street view images. The physical features and perception quality of the surrounding areas of the Chongshan campus of Liaoning University were obtained. The study found that the visual beautiful quality (VBQ) of the student living area was the highest, and the VBQ of the teacher living area was the lowest when compared to the research and study area, student living area, sports area, and surrounding area. Greenness and openness had positive influences on VBQ, while enclosure had a negative influence. This study analyzed the influence mechanism operating between spatial physical features and VBQ. The results provide theoretical and technical support for campus space spatial quality construction and improvement.