For rocket structural health monitoring, this paper proposes a damage detection method based on deep learning. This method directly takes the vibration data of multiple channels as input, and performs damage identification based on LSTM-ResNet model composed of the long short-term memory structure (LSTM) and residual convolutional neural structure (ResNet). The advantages are that firstly, LSTM is used to extract the time-dependent features of the signal, which reduces the impact caused by the lack of some channel signals, and then ResNet is used to further extract spatial features without loss of features, which improves the training efficiency and the accuracy of damage identification. In this paper, a liquid-filled cylinder vibration and water discharge experiment is used to simulate the fuel consumption of the rocket in flight. Based on self-built data sets and public data sets, the performance of LSTM-ResNet, LSTM, ResNet and ResNet-LSTM networks are compared. The training results showed that the LSTM-ResNet model was the best. It had better performance in the case of whether the sensor was faulty, and had higher damage detection accuracy.