In order to support the reliable operation of spacecraft, the ground tracking, telemetry and command (TT C) equipment need to be in power on state for a long time with the increase of the number of spacecraft in orbit, which brings great difficulty to the equipment state detection and maintenance management. Two state prediction models of GM and Verhulst TT C equipment based on grey system theory are proposed in this paper. By data smoothing and background value improvement, the state prediction model of ground TT C equipment is optimized, and the prediction accuracy is effectively improved. Through actual case analysis, the application scope of the two prediction models is sorted out to provide decision support for condition based maintenance of TT C equipment.