Oriented to the space-air-ground integration battlefield information collection scenario,a channel allocation and device matching model with the optimization objective of maximizing system efficiency and performance is established.There are correlation and coupling between channel allocation and device matching decision variables.In view of the characteristics,a two-stage resource allocation algorithm based on genetic algorithm and deep reinforcement learning algorithm (GD2A) is proposed.The effect of hyper-parameters on the performance and convergence rate of the GD2A algorithm is analyzed through simulation validation.Comparative experiments show that the GD2A algorithm ensures the reliability and security of military communications and maximizes the system utility in a 6G space-air-ground heterogeneous network.