Investigating the impact of street visual environments on young adults’ emotions is crucial for the promotion of walkable and healthy streets. However, the applicability and accuracy of existing studies are limited by a lack of large-scale sample validation. Moreover, many studies have determined emotions through subjective evaluation methods or relied solely on a single physiological indicator to assess levels of emotional arousal, neglecting the significance of emotional valence. In response, this study aims to enhance our understanding of the emotional impact of street visual environments by employing a method that integrates physiological feedback technology and deep learning. We collected videos of 100 streets from five districts in Chengdu to serve as experimental stimuli, and utilizing physiological feedback technology, we gathered data on electrocardiograms (ECG), electrodermal activity (EDA), and respiratory responses (RESP) from 50 participants as they observed these street environments. Subsequently, we applied deep learning techniques to process the video and physiological data, ultimately obtaining 500 data entries on street visual environment elements and 25,000 data entries on emotional arousal and valence. Additionally, we established multiple linear regression and multinomial logistic regression models to explore the relationship between visual street environments and emotions. The results reveal that elements such as green view factor (GVF), sky view factor (Sky VF), and sidewalk view factor (SVF) not only reduce emotional arousal levels but also facilitate the shift from negative to positive emotions, positively affecting emotional regulation. In contrast, visual enclosure (VE), vehicle view factor (VVF), and person view factor (PVF) are associated with negative emotional arousal, adversely affecting emotional valence. Moreover, the impact of specific visual environmental elements on different emotional states may vary. This study introduces a novel, multidisciplinary approach to accurately quantify the relationship between the environment and emotions, providing significant theoretical and practical insights for the development of healthier cities.

Urban seismic resilience represents a central concern in disaster risk management, reflecting the profoundly destructive nature of earthquakes. Conventional engineering-based assessment methods are often inadequate to meet the demands of risk response in densely interconnected urban systems. In this context, complex network theory offers a valuable analytical framework, as it can reveal the interdependencies among system components and the mechanisms of cascading failure, thereby providing reliable support for risk management. Following the PRISMA guidelines, this study systematically reviews the literature published up to June 2026 and synthesizes core concepts, modeling approaches, assessment techniques, and practical applications. The field is evolving from early linear indicators toward coupled network modeling across urban systems and dynamic resilience simulation, a development enabled by artificial intelligence and digital twin technologies. Nevertheless, considerable challenges remain. The limited coupling between social and physical systems constrains quantitative assessment, while data fragmentation and oversimplified models undermine the reliability of the resulting evaluations. This study underscores the systematic role of complex network theory and identifies key directions for future research on the seismic resilience of urban systems. As technological capabilities advance, the integration of system coupling mechanisms, digital twin benchmarks, and AI-assisted decision support is expected to facilitate the development of implementable, next-generation tools suited to the era of artificial intelligence.