Commercial streets play an essential role in urban public life, and their appearance constitutes an important visual attraction, which can attract people to form high-quality and active public spaces. However, in the traditional design analysis methods, limited by the existing evaluation tools, the key content of visual attractiveness analysis under dynamic conditions has rarely been paying attention to. Given this, this research adopts both objectivity and real-time eye movement perception technology to construct an evaluation method based on dynamic visual attractiveness-through the application of virtual reality technology to construct a virtual scene of a typical commercial street and detect the user’s actions in it Eye movement at a time and physiological parameters such as the number of fixation points, fixation duration, and pupil diameter changes.This paper takes the main commercial street in Huanggaihu Town in Hubei Province of China as an example to analyze the visual attraction caused by different design methods.Therefore, from the four aspects of material, shape, line, and vegetation, design suggestions for the reasonable distribution of visual attractiveness in the commercial street space design are proposed to provide useful enlightenment for the vast towns’ commercial street space design.

The immersive experience and diverse interaction modes provided by virtual digital technology match the spatial sensitivity and practical ability required in the field of architectural teaching, and its technological advantages can significantly enhance teaching outcomes. However, in the field of architecture teaching, there has been a lack of discussion on the suitability of different virtual technologies and teaching scenarios for historical buildings. Therefore, this study constructed a comprehensive adaptive evaluation system for virtual technology and selected 360-degree panoramic technology (360-degree PT), Immersive Virtual Reality (IVR) technology, and Desktop Virtual Reality (DVR) technology as representatives for empirical study. The research method used in this experiment is a questionnaire survey, involving a total of 60 students majoring in architecture and planning, with a certain foundation in historical architecture knowledge. Multiple statistical methods are employed to analyze the data. The experimental data indicates that IVR technology performs the best in terms of learning outcomes, learning autonomy, and sense of presence, making it suitable for immersive and interactive learning needs. 360-degree PT demonstrates the best perception of architectural features, catering to the demand for visual effects. DVR technology exhibits the highest learning efficiency, meeting the need for simplicity, efficiency, and widespread application. On the other hand, augmented reality (AR) technology and mixed reality (MR) technology have lower adaptability and do not meet the learning requirements for immersive spatial experiences. This study is expected to provide a new technical pathway for teaching historical buildings and serve as a systematic reference for selecting specific technologies.

As urban design increasingly focuses on higher-order human-centered needs, women-friendly cities have become an important topic in urban governance and spatial planning. Assessing perceived safety during nighttime walking is therefore crucial for understanding how urban pedestrian environments are experienced by women. This study examines perceived safety responses among young female participants in nighttime urban walking spaces, with a focus on identifying spatial factors associated with subjective safety evaluation and physiological arousal under controlled VR-based conditions. By combining semantic segmentation, BWS-based subjective evaluation, virtual reality environments, and multimodal physiological signal detection, the study explores how nighttime spatial attributes are associated with participants’ arousal, attention, and perceived safety responses. The findings provide exploratory evidence and design implications for gender-sensitive nighttime pedestrian environments. These findings should be interpreted as evidence from a controlled VR-based experiment with young female participants, rather than as population-level conclusions about all women or direct measurements of real-world nighttime risk perception.

With advancements in biology and sensor technology, neuroscience, particularly electroencephalograph (EEG) technology, has seen significant developments and applications in the field of space emotion perception. EEG technology records brain electrical activity through electrodes placed on the scalp. The signals collected by these electrodes reflect the brain’s activity status and are utilized for studying cognition, diagnosing diseases, and more. EEG technology has revolutionized the measurement of human emotions within space contexts by providing preliminary insights into human space cognition. Traditional methods, such as subjective survey questionnaires and field visits, are characterized by high levels of subjectivity, instability, and delays, which significantly undermine the objectivity, accuracy, and real-time nature of emotion recording. In contrast, EEG technology offers real-time and objective feedback on changes in space elements, effectively guiding designers to enhance space quality and improve user satisfaction and well-being within these spaces.

Predicting and evaluating the vitality level of public spaces of urban design is crucial before new construction and renewal, especially for developing countries, to avoid failures like "ghost neighborhood" and reduce resource waste. However, existing assessment methods for the urban vitality are either static and ignore changes in the time dimension, or rely on historical big data and are unable to predict unbuilt projects. This study combines previous methods and crowds’ characteristics observation in public space, puts forward the crowd-frequency hypothesis and constructs an algorithm, and establishes a time-dimensional urban vitality dynamic prediction model. Taking the Rundle Mall neighborhood in Adelaide, Australia, as an example, the effec-tiveness of the prediction model was proved through on-site observation sampling comparative verification method. In addition, the decision tree model was used to conduct machine learning on the case data, and a set of algorithm programs were obtained that can directly input some basic information to obtain the urban vitality level (high-medium-low) in a certain period. It can be used as a tool to support the design and decision-making processes of urban planners and government officials, providing a low-cost way to achieve sustainable urban vitality construction of late-developing cities directly.