###### Abstract  
Knowing the functions of buildings is valuable in urban planning and management. For example, it can be used for the assessment of urban planning implementation and the fine-tuning of community governance. At large scales, determining building functions can be time-consuming and laborious. While point of interest (POI) data can be used to identify urban building functions, it is prone to missing values. The present study proposes combining POIs with the spatial relationships between geographic entities and geographic information systems (GIS) to improve the accuracy of urban building function identification. First, the POIs are reclassified according to building functions. Second, the spatial relationships among road networks, buildings and POIs are analysed, and the frequency density ratios of POI types are calculated to identify the functions of buildings that contain POIs. Finally, buildings that do not contain POIs are identified by calculating the spatial similarity between unrecognised buildings and recognised buildings within the same road network mesh. The method can identify buildings with singular residential, commercial, office, and public services functions, as well as seven mixed functions, with an accuracy, recall, and F1 value of 90.28%, 97.52%, and 93.76%, respectively. Public service buildings and residential buildings have the highest identification precision, while the identification precision of mixed commercial and public service buildings and mixed residential and public service buildings are the lowest. An experiment demonstrates the effectiveness of the method. The results indicate that the spatial relationships between entities can compensate for missing POI data. building function; classification; POI; spatial relationship of entities +
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purposes [3; 4; 5; 6]. In the study of building function classification, some researchers have integrated multiple data sources, such as high-resolution remote sensing images, LiDAR data, and nighttime lighting data [7; 8; 9]. This is because a single type of remote sensing image is not expressive enough. It is important to consider all available data sources to accurately classify building functions. Remote sensing images can extract the physical features of buildings, but they lack semantic information and have low accuracy in identifying functional types. This limited ability can lead to errors in recognising buildings with similar physical features but different functional types, as noted in [10]. Furthermore, while building outlines can be automatically extracted from remote sensing images, there is a need to enhance the precision of this process. Automatic building classification has been attempted using topographic raster maps, cadastres, digital landscape models, and land use data. However, obtaining up-to-date map data is difficult and requires significant human and material resources, limiting their application in functional building classification [11; 12]. The arrival of the geographic big data era has brought massive online geographic information resources, such as social perception data from social media [13], cab trajectory data [14], cell phone data [15], street-view image data [16], and point of interest (POI) data [17; 18]. These record a large amount of information about human activities and have been widely used in the study of urban spatial structure characteristics. Use of social perception data to study urban spatial structures is a new approach to recognising urban building functions. Some scholars have utilised socio-perceptual data to identify urban building functions and study their fine-scale spatial layouts [19; 20]. [PERSON] used trip and stop data from traffic and public transportation databases to establish connections with surrounding buildings and infer their functions [21]. [PERSON] integrated social network data, cab trajectory data, POI data, and remote sensing imagery to identify mixed-function buildings in a city [22].