This paper presents an experimental framework for the recognition and localization of vehicle and personnel targets, involving the production of datasets and the experimental validation of the framework. The approach utilizes the short-time Fourier transform (STFT) to extract feature information from acoustic vibration signals. A residual neural network is then applied to recognize these features, addressing gradient vanishing and explosion problems. For localization, a six-element acoustic array signal is processed using the Multiple Signal Classification Algorithm (MUSIC). The framework commences with the extraction of feature information from sound and vibration signals collected by acoustic shock sensors via STFT. Subsequently, a target recognition algorithm employing residual neural networks is proposed. For localization, the method employs MUSIC to divide the signal and noise spaces by constructing a six-element array signal receiving matrix and performing eigenvalue computation, followed by spectral peak search to obtain target localization. Experimental trials validate the recognition and localization of moving targets. The overall recognition rate, taking environmental factors into account, is 96.80%. The accuracy of personnel target recognition is 89.74%, while vehicle recognition accuracy reaches 100%, demonstrating superior target recognition capabilities. In the localization experiments, the error for moving targets does not exceed 5°. Specifically, the error in personnel positioning is less than 4°, and vehicle positioning error is less than 5°, indicating accurate rotation angle solutions for positioning targets.