Accurate prediction of aviation material requirements for off-site missions is one of the main elements of a good trip assurance. To this end, this paper proposes a combination of grey correlation (GRA), improved particle swarm algorithm (IPSO) and support vector machine (SVM) as a method for predicting aviation material. Firstly, GRA is applied to analyse the factors influencing the demand for carriage of aviation materials; secondly, the particle swarm algorithm (IPSO) is improved by introducing activity factors and non-linear inertia coefficients, and the SVM parameters are optimised by IPSO; finally, the optimised SVM model is used to predict the demand for aviation materials. The simulation results show that the GRA-IPSO-SVM method has a 0.16 decrease in RMSE, a 2.18% decrease in MAPE and a 0.7s decrease in prediction time compared with the PSO-SVM method.