In order to explore an efficient method of timber species identification, the near-infrared spectral data of the eucalyptus, the Chinese fir, the larch, the Pinus massoniana and the Pinus sylvestris were selected as the research object. The qualitative identification model of timber species based on principal component analysis and support vector machine were established respectively. In the principal component analysis identification model, the 2D and 3D principal component analysis scores were drawn after preprocessing the sample spectral data. It is found that five kinds of timber species can be distinguished effectively in the principal component analysis score scatter plots, and the 3D principal component analysis score scatter plot shows the difference between the timber species more intuitively and clearly than the 2D principal component analysis score scatter plot. It is shown that the principal component analysis can distinguish the small sample timber species at the visual level. In the support vector machine identification model, the methods of genetic algorithm and particle swarm optimization were selected respectively for parameter optimization. Results showed that, the best discrimination accuracy of cross-validation was 95.71%, and the prediction accuracy rate of test set was 94.29% in the genetic algorithm-support vector machine model, which cost 134.08 s. While in the particle swarm optimization-support vector machine model, the best discrimination accuracy of cross-validation was 94.29%, and the prediction accuracy rate of test set was 100.00%, which cost 19.98 s. It indicates that the model based on intelligent algorithm and support vector machine can effectively identify the timber species. This study has made a useful exploration of the application of near infrared spectroscopy in the wood science, and provided a new method for rapid identification of timber species.