With the increasing proportion of feedback input in battlefield environment,AGNES hierarchical clustering algorithm in unsupervised learning is proposed to improve the traditional theoretical framework of multi-agent alliance.Considering the complexity and fuzziness factors in the environment and the efficiency of different individual sensors,the multi-agent model of the alliance is described and the specific prompting steps of the dynamic alliance detection system under the multi-agent cross prompt are designed.Furthermore,the cognition consistency function combined the unsupervised learning AGNES clustering algorithm with target detection information is improved,so that the system is able to directly establish a dynamic model from the changing environment,thus to achieve the long-term benefit accumulation of the alliance optimization task and make a more favorable decision for the current short-term benefit.Simulation shows that compared with the traditional overall mobilization strategy such as swarm intelligence algorithm,the improved algorithm is more in line with practical needs.