To achieve rapid automatic detection and identification of void damage in high-rise composite structures, a bridge tower full-scale model was tested for damage using Zhangjinggao Yangtze River Bridge's composite structure tower. Through numerical simulation of sound field spatial distribution, time-frequency response characteristics comparison analysis, and convolutional neural network(CNN) model training and visualization. An automatic device for void detection of high-rise composite structures and a deep learning detection method based on acoustic signals were proposed. The results demonstrate that the acoustic signal analysis method based on automatic device acquisition can be used as a new approach for automatic detection and identification of void damage in high-rise composite structures. The constructed CNN model can achieve high-precision classification of structural void state, and the recognition accuracy is 96.8%. The automatic device and intelligent detection method enable automatic real-time detection and classification of high-rise composite structures, improving automation and reducing safety risks.