Abstract: The prediction of the residual useful life (RUL) of satellites and their key components with statistical methods, such as those based on the reliability, involves some problems like modeling difficulty and low prediction accuracy. For seeking a more fast and accurate way to predict the RUL of key components of the satellite in orbit, the gated recurrent unit (GRU) network, with excellent ability of feature extraction of the time series data, is selected to build the RUL prediction model of satellite key components. In addition to use the satellite telemetry data, the satellite operation environmental data are also introduced to the model. At the same time, to improve the prediction accuracy, the method of convolutional neural network (CNN) is combined with the GRU. The proposed new model enjoys the respective advantages of the CNN in the feature extraction and the GRU in the time series prediction in improving the prediction accuracy. Finally, with the whole lifetime telemetry data of antenna transponder of a certain satellite as an example, the prediction accuracy is shown to be significantly improved with the combined CNN-GRU model for RUL prediction.