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BackgroundThe obesity rate of Beijing residents has been rapidly increasing in the past decades. The incidence rate of diseases caused by obesity is high, and resident health is a concern. According to public data and published studies, obesity may be related to the urban built environment. Methods In this study, a data set of "resident health–urban built environment" based on the data of surveying and mapping in the Shijingshan district of Beijing and the registration data of obesity status from top medical institutions in Beijing is developed. The data of the urban built environment includes five factors: the economic value of location, mixture of land use, facility layout, spatial characteristics of streets, and subjective evaluation data of residents. Additionally, a multi-dimensional judgment matrix of obesity of residents and built environment indicators is established. The random forest algorithm of machine learning is used to classify the data set, and the correlation and contribution of the influence of each environmental factor on the obesity are judged. Results For all communities in the Shijingshan district, the street betweenness and street curvature make the largest contributions to obesity among all spatial characteristics, whereas the number of parks and water-related sceneries in the living environment contributes the least. Conclusions The calculated contributions of different environmental factors to obesity can be directly employed to promote a healthy lifestyle. On the premise of intervention cost-saving, similar effects can be achieved by improving the spatial environment of streets and layout of facilities rather than applying intervention measures of the overall change.