Aiming at the characteristics of large-scale combat resource service pools and low service organization efficiency in the command information system under the SOA architecture, a clustering study of service organizations is conducted. Firstly, the attributes of combat resources are analyzed, based on the OWL-S description specification, serviceoriented modeling of combat resources is formed, and an atomiclevel service ontology model of combat resources is formed to realize the cloud service of combat resources. Then a clustering model of combat resource service is constructed, and the simulated annealing method and the K-means method are combined in the genetic algorithm, and a clustering method of combat resource service is proposed. Finally, the simulation is compared with the GS and GK algorithms. The experimental results show that the proposed algorithm can find a clustering scheme with a higher fitness value within an acceptable time, and the standard deviation of the fitness value of the solution results is lower. Compared with GS and GK algorithms, this method has better optimization and stability.