Wood species can be classified according to thedifferences in spectral reflectance of different wood surfaces. To prevent wood corruption or cracking, glorify the appearance, and prolong the service time of wood products, wood surfaces often need to be coated with paints in the production practice of wooden furniture and handicrafts. The influence of paints on the spectral reflectance of wood surfaces will lead to the drift and transformation of spectral curves. The experimental results show that the classification model trained by the spectral reflectance of the original wood samples cannot be used to classify the painted wood. The drift and transformation of spectral curves of painted wood compared with original spectral curves without coated paints can be fitted by nonlinear models such as neural networks. To continue to use the classification model trained by the original wood dataset, we use a fully connected neural network to fit the relationship model between the original spectral reflectance and the coating spectral reflectance. Through this model and SVM classifier, the coating spectral reflectance is corrected to realize the classification of painted wood using the classification model trained by the original wood spectrum. We also use the convolutional neural network model to extract the convolutional features of spectral reflectance and add a hidden layer based on the relationship between the convolutional features of original spectral reflectance and coating spectral reflectance to modify the convolutional features of coating spectral reflectance and output the classification results through the output layer. To verify the effectiveness of the proposed scheme, we collect the near-infrared spectral reflectance (NIR: 950~1 650 nm) and visible/NIR spectral reflectance (VIS/NIR: 350~1 000 nm) of 20 wood species and compare the performances of the rectification models for 8 different paints in terms of the corrected spectra. Because of the experimental results, it can be concluded that the classification performance of NIR is better than that of VIS/NIR. The correction model based on the convolutional neural network can improve the NIR classification accuracy of the transparent wood surface to more than 70%. In contrast, the fully connected neural network can improve that to more than 80%, but neither model can correct non-transparent coatings on the wood surface. Regarding training speed and recognition efficiency, the correction model based on a convolutional neural network is better than that based on a fully connected neural network. In summary, the nonlinear relationship model between the original spectral reflectance and the coating spectral reflectance established by these two neural networks can correct the coating spectral reflectance with clear paints and then classify the painted wood directly by the classification model trained by the original spectral reflectance. The proposed scheme extends wood species classification from the original wood product to coating wood with clear paints with certain practical application significance and prospect.