<p>The advantages of guided wave detection, such as its ability to propagate over long distances and penetrate deeply, have led to its application in the field of anisotropic damage detection in carbon fiber-reinforced polymer (CFRP). Due to the anisotropy of CFRP, traditional guided wave-based detection methods have difficulty in precisely locating the defect. In this study, we proposed a novel deep learning-based detection method for CFRP by employing image recognition technology for guided wave field inspection. This method is capable of rapidly and accurately extracting defective features from the structure, thereby facilitating precise damage identification. To avoid time-consuming sample data generation by simulation for CFRP, the steady-state guided wave field of the aluminum plates was simulated instead. The isotropic wave field data were then stretched and applied for neural network training.</p>