<p>The acoustic signal feature extraction and intelligent diagnosis method for tile debonding are of great significance to ensure building safety. This paper presents a detection method based on wavelet transform and Convolutional Neural Network (CNN) integrated with Support Vector Machine (SVM) to solve the problem that the detection results of traditional defect detection methods based on frequency domain characteristics are unstable under environmental noise imbalance. In this study, the acoustic vibration signals polluted by real environmental noise were collected. The time–frequency diagrams were obtained using the complex Morlet wavelet transform, which captured the temporal and spectral variations of the acoustic signals. The image recognition method of CNN was enhanced through the integration of SVM, which replaces the softmax classifier with SVM. Four tile-wall specimens with different degrees of debonding were crafted, and the tiles were divided into nine different regions for tapping. Model training and prediction were conducted on the acoustic signals acquired from identical regions across the four specimens, which verified the reliable classification performance of this method. The average test accuracy of the nine regions reached over 98%, which provides a basis for the study of debonding quantification. Moreover, the traditional CNN was also employed for model analysis, and comparative result revealed that the proposed method demonstrates superiority in accuracy and efficiency. In the future, more groups of experiments on debonding area gradients could be conducted to research whether this method can accurately classify the degree of bonding defects when the obtained data set is large and sufficient.</p>