The work aims to solve such defects as the slow convergence speed of image feature sparse coding, and large retrieval error induced by insufficient local feature space information in current image retrieval technology. An image retrieval algorithm based on l0 sparse constraint non-negative matrix factorization coupled visual dictionary optimization was proposed. Firstly, on the basis of the non-negative matrix factorization (NMF), l0-constraints were stetted on the coefficient matrix to limit its sparsity, so that a NMF framework for l0-sparse constraints was defined. Then, an initialization scheme of adaptive sequence dictionary was proposed to obtain the initial estimation of the dictionary from the training samples. Then, the NMF with l0 sparse constraints was used to enhance the visual dictionary for sparse coding on image local descriptors, and the polymerization feature vectors were generated by the maximum pooling operation to retain the key attributes of the local descriptor. Finally, according to the obtained feature vectors, the Minkowski distance was introduced to measure the similarity between the query image and the database for outputting the retrieval image. The experimental results showed that the proposed algorithm had a higher precision-recall rate and faster convergence speed compared with the current image retrieval scheme. The image retrieved by the proposed algorithm has a high similarity to the query image, which has a certain reference value in the fields of package trademark retrieval, etc.