In this paper, a sparse autoencoder (SAE) network is proposed to extract the effective features of vibration signals and apply them to support vector machine (SVM) to detect footstep vibration events, aiming at the problems of low accuracy and manual parameter selection of traditional short-time energy detection method. To alleviate the signal distortion caused by the dispersion effect of vibration signal, the wavelet decomposition method is used, and the decomposition parameters are optimized based on the experimental analysis, and then the location is solved based on the generalized cross-correlation (GCC) and time difference of arrival (TDoA) algorithm. Experimental results show that, compared with manual feature screening, the detection accuracy of an active segment can reach 96.8% by the SAE-SVM algorithm, and the average positioning error of the system is 0.82 m.