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Long-standing attention has been given to urban vitality and its association with the built environment (BE). However, the multiplicity and complex impacts of BE factors that shape urban vitality patterns have not been fully explored. For this purpose, multisource data from 1025 communities in Wuhan, China, were combined to explore the BE vitality nexus. A deep learning method was explored to segment street-view images, on which a composite indicator of urban vitality was developed with social media data. Then, six dimensions of BE factors, neighbourhood attributes, urban form and function, landscape, location, and street configuration, were incorporated into a spatial regression model to systematically examine the composite influences. The results show that population density, community age, open space, the sidewalk ratio, streetlights, shopping and leisure density, integration, and proximity to transportation are positive factors that induce urban vitality, whereas the effects of road density, proximity to parks, and green space have the opposite results. This study contributes to an improved understanding of the BE nexus. Managerial implications for mediating the relationship between planning policies and urban design strategies for the optimization of resource allocation and promotion of sustainable development are discussed. • Explore the BE-vitality nexus by integrating advanced methods and multi-dimensional BE factors. • Combine a rich variety of big-data to clarify the composite effects of BE factors on urban vitality. • Compose a composite indicator to measure urban vitality by joining street movements and social media data. • Integrate spatial regression and GWR models to reduce the bias caused by spatial autocorrelation and heterogeneity. [ABSTRACT FROM AUTHOR]