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Aiming at the difficulty in feature extraction from pipeline vibration signal in complex environments, a pipeline defect pattern recognition method based on Long Short-Term Memory network (LSTM) deep learning neural network is proposed here. Firstly, the collected original signal is decomposed for several intrinsic modal function (IMF) components with the Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN). Then the approximate entropy of the IMF component is calculated according to the information entropy theory as the eigenvalues of the pipeline running state to construct the feature vector set. And then the typical LSTM deep learning neural network training model is constructed and the relevant parameters of the deep neural network amid the training process are adjusted to optimize the network structure. Finally, the feature vector is input to the LSTM neural network model for training and recognition. The research results show that: for the problem that the pipeline vibration signal features are weak and difficult to extract, the accuracy of the method for pipeline defect pattern recognition has reached 95%, and it has obvious advantages in eliminating the background noise of the pipeline vibration signal, mining feature information, and ensuring recognition accuracy.