Addressing the location selection problem for urban logistics distribution centers, incorporation of urban dynamic development scenarios and unmanned aerial vehicle(UAV) characteristics is crucial to enhance solution scientificity and practicality. First, three dynamic demand scenarios—organic growth, radial expansion, and directional development—were established based on urban development patterns. A full-coverage location model minimizing total location and operational costs was formulated. Subsequently, the traditional K-Means algorithm was enhanced using a grid-based method to improve solution accuracy. Numerical simulations demonstrated model feasibility and effectiveness, revealing that: (1)The improved K-Means achieved higher clustering accuracy, reducing transportation energy consumption by up to 11.87%; (2) Dynamic strategy yielded 12.30%–34.43% lower total costs than static strategy (minimal reduction in radial expansion; maximal in organic growth); (3) Dynamic strategy outperformed static strategy except under extreme instances (e.g., abnormally high construction costs or single-year demand concentration).