Objective：Sparse coding(SC) is unstable in the process of encoding, besides image representation and classification are relatively independent in SC method, a new method is proposed, called image classification with non-negative and local Laplacian sparse coding and context information (NLLSC-CI). Method：Firstly, non-negativity and locality constrained Laplacian sparse coding (NLLSC) is used to encode local features in order to obtain original image representation by max pooling (MP), which improves the instability of encoding effectively. Some image representation that is randomly selected from all images is generated joint spaces based on context information, and all images are mapped into these spaces by SVM classifier. The mapped feature representation is regarded as final image representation, which solves the problem that image representation and classification are relatively independent and preserves more context information between features from images. Result：In order to verify the efficiency of the proposed algorithm, four public image datasets including Corel-10, Scene-15, Caltech-101 and Calthch-256 are used to experiment. The results are compared with state-of-the-art sparse coding algorithms, which have suggested the classification accuracy increases 3%~16%. Conclusion：The proposed algorithm improves the instability of coding and preserves the mutual dependency between local features. Experimental results have shown that our algorithm has better performance than some previous algorithms have been developed. In addition, the novel method has proved capable of application to numerous computer vision such as image segmentation, image annotation and image retrieval.