Each spatial clustering algorithm has its own advantages and disadvantages. Spatial clustering algorithms can be improved and optimized through the fusion of the algorithms advantages. A spatial clustering method fusing multiple algorithms for area feature is proposed in this paper. This method optimizes the initial cluster centers of K-means algorithm by using genetic algorithm and other optimization algorithms

selects the k value of K-means algorithm by using a fast clustering algorithm density-based

and then obtains spatial clustering results with improved K-means algorithm. It improves the genetic algorithm which is easy to be affected by the initial population and has low efficiency. The experimental results indicate that the method is steady

and is more efficient and accurate compared to the traditional algorithm.