AbstractRecently, China’s urban construction has gradually shifted from incremental development to quality improvement, and urban residents’ demand for a better travel environment has become stronger. Therefore, as the main carrier of urban slow traffic and an important part of public space, streets are gradually becoming the focus of urban planning. This study starts from the perspective of the residents’ needs and selects the measurement indicators from multiple dimensions. The combination of big data analysis and field observations makes up for the shortcomings in the subjective methods, single measurement dimensions, and low data accuracy in previous studies and provides a comprehensive measurement method for the spatial quality of typical streets in China, and provides theoretical support and a scientific basis for urban sustainable development. This study used the Bijiashan Block, Hefei, Anhui Province, China, as an example. Based on the street view big data and technologies, such as point of interest (POI) spatial analysis, road network accessibility analysis, and machine learning algorithm, this study conducted a quantitative evaluation of the street space quality from four dimensions: (1) visual perception; (2) functional attribute; (3) road network structure; and (4) spatial form. In addition, quantitative measurements of pedestrian walking characteristics are conducted based on pedestrian flow rate and walking speed. Correlation and regression analyses are adopted to explore the correlation between street space quality and pedestrian walking characteristics and the impact of each indicator. The results indicate that street richness, green visibility, POI density, and vehicle interference degree show a significant correlation with walking characteristics and indicators, such as street accessibility and functional mixing degree that are believed by the public to affect walking characteristics show no significant correlation with street scale.