To address the challenges of difficult visual management and inefficient localization in large-scale， multi-category urban spatiotemporal data， an interactive visualization method for multi-category urban spatiotemporal big data based on point aggregation was proposed. Firstly， two efficient aggregation methods were introduced： geographic location-based aggregation and geographic hierarchy-based aggregation， to meet the government personnel’s needs for efficient visual management across diverse scenarios. Secondly， on the basis of efficient point aggregation， a conditional parsing algorithm was proposed o enable real-time parsing and conversion of spatiotemporal conditions and category visibility， improving data localization efficiency. Finally， 270 000 pieces of urban entity data from Beijing were used to conduct geographically hierarchical parsing algorithms and aggregation interaction experiments. Experimental results demonstrate that the average processing time of the two proposed aggregation methods were reduced by approximately 69.66% and 63.15%， respectively， compared to K-means in different scenarios， confirming the efficiency and stability of data processing and aggregation services in the system during data storage. Besides， this aggregation application service has been successfully deployed in a city governance project， supporting million-scale urban data aggregation with proven applicability.