Cities are the main carriers of high population agglomeration and socio-economic activities and are also the areas where contradictions among production, living, and ecological space are concentrated. Effective identification of production–living–ecological space is conducive to the balanced and sustainable development of urban space. First, this paper analyzes the formation mechanism and connotation of urban production–living–ecological space and constructs the classification system of point-of-interest (POI) data. Then, it identifies the production–living–ecological space in the central urban area of Wuhan effectively by using the analytic hierarchy process, spatial analysis method, and the quadrat proportion method and verifies the accuracy of production–living–ecological space by the sampling verification method. Last but not least, it adopts spatial auto-correlation analysis and Geo-detector to reveal spatial heterogeneity and its driving factors. The results indicate that: (1) The overall accuracy of the identification accuracy test of production–living–ecological space in Wuhan is 92.86%. (2) There is a significant spatial correlation among production space, living space, and ecological space in the central urban area of Wuhan with living space being the dominant space and production space the secondary space intersected and embedded in the north and south banks of the Yangtze River. (3) Results of the analysis of the driving factor show that elements comprising life services, corporate enterprises, and scenic spots play a leading role in realizing the living space, the production space, and the ecological space, respectively, and the interactions between these elements have a significant driving effect on the three types of space. The results prove that POI big data are more scientific and practical in urban spatial planning, and it can provide a useful reference for the sustainable development of spatial planning.

As urban spatial patterns are the prerequisite and foundation of urban planning, spatial pattern research will enable its improvement. The formation mechanism and definition of an urban “production–living–ecological” space is used here to construct a classification system for POI (points of interests) data, crawl POI data in Python, and DBSCAN (density-based spatial clustering of application with noise) to perform cluster analysis. This mechanism helps to determine the cluster density and to study the overall and component spatial patterns of the “production–living–ecological” space in the central urban area of Wuhan. The research results are as follows. (1) The spatial patterns of “production–living–ecological” space have significant spatial hierarchical characteristics. Among them, the spatial polarizations of “living” and “production” are significant, while the “ecological” spatial distribution is more balanced. (2) The “living” space and “production” space noise points account for a small proportion of the total and are locally clustered to easily become areas with development potential. The “ecological” space noise points account for a large proportion of the total. (3) The traffic accessibility has an important influence on the spatial patterns of “production–living–ecological” space. (4) The important spatial nodes of each element are consistent with the overall plan of Wuhan, but the distribution of the nodes for some elements is inconsistent. The research results show that the POI big data can accurately reveal the characteristics of urban spatial patterns, which is scientific and practical and provides a useful reference for the sustainable development of territorial and spatial planning.