Satellite cloud imagery can show the features and the evolution processes of all kinds of cloud systems from different aspects. Thus, adopting the content-based cloud image retrieval makes a big difference in supervising present weather conditions and studying the climate change. In order to optimize the combined features of the cloud picture and strengthen the generalization ability of its combined features, this paper presents an optimal method of combining the features of the sparse representation with the subspace projection. At first, we should extract its color, texture and shape, convert all the combined features, and divide them into different blocks. Then, we can make the sparse representation for each block’s features, grouping them according to different atom variance and gaining both noticeable and unnoticeable features. Finally, we can count the power of the grouped features to get the subspace projection matrix, projecting the original combined features on it and achieving the optimal cloud picture features. The experiment turns out that the method of optimizing the cloud picture features in this paper is better than common descending dimension method and cloud retrieval technology in precision ratio and recall ratio. It indeed has a stronger optimization in the combined features as well as a lower time complexity in the process of the real-time retrieval, which indicates a brand new retrieval method.