In order to improve the recognition accuracy of intrusive vibration events by a distributed fiber-optic acoustic sensing system(DAS) based on a phase-sensitive optical time-domain reflectometer (-OTDR),a recognition approach based on empirical modal decomposition (EMD) combined with a one-dimensional convolutional neural network (1-D CNN) is proposed.The EMD is firstly used to decompose the vibration signal into m-order intrinsic mode function (IMF),then the Pearson correlation coefficient (PCC) is used to determine the effective IMF components,the effective IMF components are denoised using the wavelet threshold denoising algorithm (WTD),the reconstructed signal is obtained by summing up all the denoised IMF components,and finally the 1-D CNN is used to identify the reconstructed signal.It is proved that this identification method can quickly complete the training of the identification model,the training time is less than 3 min,and can effectively identify the intrusion vibration signals collected in the real environment,and the identification accuracy of the intrusion signals can reach 98.3%.