Traditional sparse coding has proven to be an effective method for image feature representation in recent years, yielding promising results in image classification. However, it faces several challenges, such as sensitivity to feature variations, code instability, and inadequate distance measures. Additionally, image representation and classification often operate independently, potentially resulting in the loss of semantic relationships. To address these issues, a new method is proposed, called Histogram intersection and Semantic information-based Non-negativity Local Laplacian Sparse Coding (HS-NLLSC) for image classification. This method integrates Non-negativity and Locality into Laplacian Sparse Coding (NLLSC) optimisation, enhancing coding stability and ensuring that similar features are encoded into similar codewords. In addition, histogram intersection is introduced to redefine the distance between feature vectors and codebooks, effectively preserving their similarity. By comprehensively considering both the processes of image representation and classification, more semantic information is retained, thereby leading to a more effective image representation. Finally, a multi-class linear Support Vector Machine (SVM) is employed for image classification. Experimental results on four standard and three maritime image datasets demonstrate superior performance compared to the previous six algorithms. Specifically, the classification accuracy of our approach improved by 5% to 19% compared to the previous six methods. This research provides valuable insights for various stakeholders in selecting the most suitable method for specific circumstances.