Aiming at the requirements of precise prediction and health management of spacecraft, a method for combinational prediction of parameter values called particle swarm optimization grey neural network is promoted. The method enables particle swarm optimization algorithms, grey theory and neural network to complement each other. Firstly, a prognosis for output current values of southern sailboard of a certain satellite is taken as an example. Then, three evaluation indexes of prediction, including mean absolute error, mean absolute percentage error and root mean square error, are chosen to evaluate the results of different step length prediction is of particle swarm optimization fuzzy neural network. The results show that the particle swarm optimization fuzzy neural network is effective. Secondly, the mean absolute percentage errors of particle swarm optimization fuzzy neural network, grey model particle swarm optimization neural network, particle swarmoptimization neural network and grey model are calculated. The results show that the model of particle swarm optimization fuzzy neural network is the most precise one and more efficient in prediction than others. It has vast application prospects in the field of prediction of spacecraft parameter values.