###### Abstract
[PERSON], [PERSON] [14] built a feature vector using quantized information entropy, energy, and variance utilizing the wavelet package calculation. The results revealed that the feature is helpful for stress wave signal singularity detection. From the above studies, it can be concluded that in the processing of foundation pile dynamic measurement signals, the feature values extracted are all from the same dimension, mostly variance, power spectrum means, and information entropy, which does not differentiate enough between defects. However, the defect information in the foundation pile detection signal under layered soil conditions is very complex and rich. It is often insufficient to characterize the complex signal with only one-dimensional features, so a more in-depth study is needed in extracted features. Machine learning is frequently utilized in defect recognition as science and technology advance. Support vector machines are well suited to the learning method for small sample situations, cleverly solving the dimensionality problem and avoiding dimensional disasters. [PERSON], [PERSON] [15], [PERSON], [PERSON] [16], and [PERSON] [17] used support vector machines to identify the type and degree of foundation pile defects based on the minor sample nature of the test data, which improved the accuracy of the classification of foundation pile defects. Deep learning is also advancing quickly, and neural networks, with their strong self-learning and nonlinear mapping capabilities, are particularly suitable for recognizing complex signals. Good results have been achieved in recognizing foundation pile defects [18; 19]. It is challenging to show during foundation pile integrity testing when the engineering properties of the soil on the pile side change significantly whether the characteristic signals on the curve are brought on by pile defects or geological changes simply based on the reflected wave curve in the field [20]. To avoid the interference of soil formation on the foundation pile integrity and improve the inspectors' efficiency, this paper analyses the changing pattern and characteristics of the dynamic measurement curve of the pile body with common defects by establishing a three-dimensional finite element model and verifies the numerical simulation using experiments. The article combines multi-feature extraction and a convolutional neural network to achieve an accuracy of 97.8% in recognizing foundation pile defects, which realizes the automated detection of foundation pile defects. The quality problem of foundation pile engineering will involve various aspects. Consequently, the state places a high value on the quality inspection department; yet, due to testing expenses and project progress limitations, the quality inspection department usually uses random inspections to control the quality of the project. Random inspection results tend to miss some defective foundation piles, and the inspection results lack impartiality. It is essential to research and explore methods for testing foundation piles at a lower cost and a higher level of inspection. Therefore, this research has good theoretical and practical application value and is highly important in terms of both societal and economic rewards.