A fault prediction method for electric submersible screw pumps was proposed,addressing the challenges of timely fault detection and accurate fault type identification.The method combined long short-term memory networks (LSTM) and probabilistic neural networks (PNN).The LSTM network was employed as a regression model to predict the future trends of fault signals using time series analysis.The faulty signals of the screw pump were processed using wavelet packet decomposition to extract the fault features.Multiple operating parameters such as oil pressure and production yield were combined to construct the fault feature vector for the electric submersible screw pump.The PNN network was then utilized to classify and identify the predicted fault signals.A dataset of 120 sets of failure data from the Xinjiang Oilfield was collected for training the prediction model,and 90 sets of data were taken out as a fault database to train the model,30 sets of data were selected as the test set to evaluate the accuracy of the model.The LSTM-PNN neural network prediction model was applied to predict the faults in the electric submersible screw pumps using the two groups of data separately.The results show that performing fault feature extraction on the fault signals can effectively improve the accuracy of fault prediction for electric submersible screw pumps.Compared to traditional methods of fault prediction,the LSTM-PNN network demonstrates better predictive performance and its accuracy increases from 3% to 16%.