Facing the problems of complex types of peacekeeping equipment,complex and harsh peacekeeping environment,and prominent contradiction in support maintenance,it is necessary to strengthen the demand prediction of spare parts for peacekeeping equipment support before the start of the mission.Firstly,the characteristics of peacekeeping mission are analyzed,and the concept of peacekeeping equipment support is put forward.Secondly,the steps of peacekeeping equipment support demand prediction are proposed,and the prediction model of peacekeeping equipment support based on particle swarm optimization (PSO)-back propagation (BP) neural network is studied.Finally,the effectiveness of the algorithm is verified by experiments based on actual data.The experimental results show that the prediction model of peacekeeping equipment support based on PSO-BP neural network can effectively predict the demand for spare parts of peacekeeping equipment support and improve the efficiency of peacekeeping equipment support.