The original K-means clustering algorithm is seriously affected by initial centroids of clustering and easy to fall into local optima. To solve this problem, an improved K-means clustering algorithm based on Adaptive Cuckoo Search (ACS), namely ACS-K-means, was proposed, in which the search step of cuckoo was adjusted adaptively so as to improve the quality of solution and boost speed of convergence. The performance of ACS-K-means clustering was firstly evaluated on UCI dataset, and the results demonstrated that it surpassed K-means, GA-K-means (K-means based on Genetic Algorithm), CS-K-means (K-means based on Cuckoo Search) and PSO-K-means (K-means based on Particle Swarm Optimization) in clustering quality and convergence rate. Finally, the ACS-K-means clustering algorithm was applied to the development of heat map of urban management cases of Qingxiu district of Nanning city, the results also showed that the proposed method had better quality of clustering and faster speed of convergence.