Aiming at the problems in service environment, damage situation of oil and gas pipelines in desert burial ground and the invasion situation of third party which threatens the safety of pipelines, which easily causes the difficulties in effective feature extraction and accurate classification identification of invasion vibration signals, a feature identification method for invasion signals from oil and gas pipelines in desert burial ground is proposed. The method first uses distributed optical fiber to acquire the intrusion vibration signal along the pipeline, and then decomposes the vibration signal by the modified ensemble empirical mode decomposition (MEEMD) method to obtain the intrinsic mode function (IMF) component of the signal; then extracts the energy of the IMF component and the MEEMD energy entropy to form a feature vector; finally, the feature vector is input to the extreme learning machine (ELM) classification recognition model. The experimental results show that the method can achieve the recognition of four types by tapping the pipe, manual mining, mechanical construction and sandstorm weather, and compared with BP neural network and support vector machine recognition models, the total recognition accuracy of the method reaches 94%, and the recognition speed is faster. The proposed method has important reference significance for distributed fiber optic desert buried oil and gas pipeline monitoring.