In order to solve the difficulty of segmenting the linear guide surface defects from image with a complex background, a method based on gray level co-occurrence matrix (GLCM)and non-negative matrix factorization (NMF) to suppress the texture background to realize defect feature enhancement was proposed. Firstly, the GLCM multi-feature statistics was used to reconstruct the background texture map of the linear guide surface to achieve a certain degree of texture background suppression. Then, the texture was divided into several sub-image blocks, and a certain sub-image block was randomly selected for NMF dimension reduction. Next, the basic matrix decomposed by NMF was traversed by the same size image block in the texture map to find its Euclidean distance, and the averaged distance was assigned to the center pixel of the corresponding image block in the texture image to realize texture background suppression and features enhancement. Finally, the defects were classified based on K-means clustering and support vector machine. In the experiment, the recognition accuracy of scratches, cracks and crash defects in the test set are 89.06%，88.46% and 95.12%, which shows that the proposed method can suppress the texture background effectively and enhance the defect features of the linear guide surface image, and it can separate the defects and identify their types accurately.