With the rapid growth of the economic level, people’s demand for mahogany products is increasing day by day. As precious mahogany, criminals replace sandalwood and counterfeit it because of its spiritual pursuit and high price. To maintain the order of the timber market and the interests of consumers and realize the rapid non-destructive detection and identification of sandalwood, it is necessary to establish a fast and reliable intelligent identification method of sandalwood. This paper used near-infrared spectroscopy to extract the spectral information of Pterocarpus santalinus and its similar blood sandalwood. The qualitative analysis method, partial least squares discriminant analysis ( PLS-DA ), and error back propagation artificial neural network ( BPNN ) were used to establish the calibration model of spectral information. Then, Pterocarpus santalinus and its similar wood blood sandalwood were identified. By analyzing and comparing the advantages and disadvantages and recognition accuracy of these three models for these two kinds of wood recognition, the feasibility of this method in the recognition of sandalwood and sandalwood is verified. The experimental results show that the three discriminant models can quickly identify the wood spectral images, and can quickly and non-destructively classify and identify sandalwood and sandalwood. In the case of selecting different image processing methods, the results of the three discriminant models are different, and the recognition results are also different. Further analysis reveals that when the spectral data are modeled using the BPNN model, the peak and trough eigenvalues of the original spectral data within the wavelength range of 866~2 533 nm are utilized as input following preprocessing. It is observed that when the number of input layer nodes is Setto 24 eigenvalues and the hidden layer consists of 13 neurons, the model achieves the smallest root mean square error, with an accuracy rate of 96.43%. Additionally, shortening the spectral range does not result in an improvement in the model’s recognition rate. The BPNN model demonstrates the highest recognition rate across the full band range among the three models. The experimental results indicate that the combination of artificial neural network modeling and near-infrared spectral feature extraction technology yields a high recognition accuracy in identifying sandalwood wood, surpassing trevious methodologies. This study contributes to mitigating the subjectivity inherent in manual recognition processes. The utilization of computers can expedite the recognition process, while the enhanced accuracy aids in maintaining order within the wood market and safeguarding consumer rights. Simultaneously, it offers insights into realizing intelligent visual recognition of sandalwood and furnishes technical support for the sustainable development of the mahogany industry.