Identification of ancient wood is crucial for historical research and the preservation of cultural heritage. This study focuses on identifying three ancient wood samples from the Ming Dynasty of China using terahertz (THz) technology and a convolutional neural network (CNN). A comparative analysis was conducted to identify these unknown ancient woods by comparing them with three types of modern pine and three types of modern fir. The terahertz absorption coefficients of nine types of wood samples were first calculated, followed by an analysis of their frequency characteristics within the 0.01-2.5 THz spectral range. The THz spectra were then preprocessed using wavelet denoising (WD) and low-pass filtering (LPF). Dimensionality reduction was subsequently applied to the spectral data based on a cumulative variance contribution threshold of 95%. Finally, the CNN model was developed to identify ancient wood species by minimizing root mean square error (RMSE). Results demonstrate that ancient wood samples THKM7 and NTGSMO1 share five characteristic frequencies with modern pine and fir and a consistent upward trend in absorption coefficients. In addition, the absorption coefficient of ancient wood NTSO3 shows significant deviations in frequency points and amplitudes. Furthermore, the CNN prediction results reveal the minimal RMSE values between the ancient wood THKM7 sample and modern pine XS sample (RMSE = 0.0143) and between the ancient wood NTGSMO1 sample and modern fir LS sample (RMSE = 0.0265). Finally, the accuracy of the prediction results was verified by Generalized Regression Neural Network (GRNN) and Random Forest (RF) classifiers. The study integrating THz technology with deep learning provides a research idea for ancient wood identification and can advance scientific research in cultural heritage conservation.