To improve the accuracy of wood species classification, a method is proposed based on I-BGLAM (Improved-Basic Gray Level Aura Matrix) texture features and spectral features fusion in this paper. Experimental data are hyper-spectral images in the visible and near-infrared spectral band (i. e., 372.53~1 038.57 nm) obtained by SOC710VP hyper-spectral imaging system. Firstly, the feature band selection method based on OIF (Optimum Index Factor) was used to reduce the dimension of hyper-spectral images and select the band containing a large amount of information. Secondly, NSCT (Nonsubsampled Contourlet Transform) and inverse transformation of NSCT were used to obtain the fusion image for the selected band images, and I-BGLAM was used to extract its texture features for the obtained fusion image. At the same time, the average spectrum of the whole band of hyper-spectral image was obtained, and the spectral characteristics were obtained by S-G (Savitzky-Golay) smoothing. Finally, the obtained texture features and spectral features were fused and sent to ELM (Extreme Learning Machine) for classification. In addition, the method proposed in this paper is compared with the traditional method of wood identification based on GLCM (Gray Level Co-occurrence Matrix) and the mainstreams method proposed in the field of wood species identification in recent years. There are two main innovations in this paper. One is to use the strong texture extractor I-BGLAM to extract its texture features from hyper-spectral images; the other is to propose a new feature fusion model for the classification of hyper-spectral images. The experimental results of 8 tree species show that the accuracy of using I-BGLAM to extract texture features for classification was up to 88.54%, while the accuracy of using GLCM to extract texture features was up to 76.04%. The results show that the use of I-BGLAM in this paper is better than that of GLCM in texture feature extraction, which lays a good foundation for the fusion model established later. The accuracy of classification by using the average spectral features alone can reach 92.71%. The classification accuracy of the proposed feature fusion method can reach up to 100%. This shows that it is better to use the fusion model proposed in this paper for classification than to use the classification model of a single feature. In addition, the classification accuracy obtained by using the method proposed in this paper is higher than the other two mainstream recognition methods in this field. Therefore, the method proposed in this paper based on I-BGLAM texture feature and spectral feature fusion can improve the accuracy of wood species classification, which has certain utilization value in the classification of wood species.