Abstract: Abstract: Rotor parts have been the central components of "intelligent emergency water supply and water purification integrated equipment in mountainous areas and remote disaster areas". However, various failure modes can inevitably occur, due to high-speed and heavy-duty conditions for a long time in the field. The fault characteristic frequency is also easily submerged by the strong noise, depending mainly on the inner structure of the equipment. There is a high similarity in the fault vibration signals of rotor parts, such as impeller, bearing, and gear. The existing fault identification cannot quickly and accurately detect the fault mode. In this study, an improved adaptive fault identification model was proposed to realize the intelligent fault diagnosis of multi-stage high-pressure lift pumps using a one-dimensional convolutional neural network and long-term memory network (CNN-LSTM). In the CNN network, the last set of convolutional layers used for the feature extraction was then connected to the global mean pooling layer, in order to reduce the number of model parameters and integrate feature information. Relu activation function was then selected for the gradient disappearance, compared with the Sigmoid function. As such, the activation function of the pooling layer was changed to the Relu function in the CNN network. After that, the LSTM time series network was embedded to integrate the one-dimensional CNN and LSTM into a framework structure. Finally, the Softmax function was used as the classification output of the network model. The fault identification was as follows. First, the vibration signal was collected to preliminarily de-noise through CEEMD, where the de-noised data was divided into time series of equal length. Then, the data set was divided into the training, test, and validation set. Secondly, the optimal initial parameters of the model were obtained by Bayesian optimization. The model parameters were also fine-tuned layer by layer using back propagation. The model performance was evaluated by the validation set. Third, the test set was input into the trained model to calculate the evaluation index. The fault identification experiment of multistage high-pressure pumps was carried out to verify the improved fault identification model. Four typical faults were selected to identify: spalling of rotor bearing outer ring, fracture of bearing inner ring, blockage of the impeller, and broken tooth of the gear. The analysis results show that the improved model performed better to separate fault modes, thereby realizing the accurate identification of the health status of multistage high-pressure pumps. The identification accuracy of the improved model for the four fault modes was close to 100%, indicating higher accuracy of identification and feasibility. An adaptive intelligent fault identification model was fully utilized for the multistage high-pressure water pump. Two neural networks were integrated to adaptively extract the features from the time series data and fault sample information, where the 1D-CNN output was used as the LSTM input. There was no need the feature extraction using prior knowledge. The interference of inappropriate features was also reduced for end-to-end fault identification and classification. Consequently, the superior overall performance was achieved in the improved model, where the structure was much simpler, the iteration was faster, better generalization ability, the higher resistance to the noise. High accuracy was reached more than 97%. Anyway, the improved model can be expected to serve as the fault identification network for the multistage high-pressure numerical key fault of rotor bearings, wheels, and gears.