The work aims to define a trademark retrieval scheme based on region growth coupling multi-classifier for the semantic gap in the trademark retrieval algorithm and the low accuracy of trademark retrieval due to low correlation between the underlying visual features and the high-level semantics leads to. Firstly, the input trademark was preprocessed to remove noise and spurious points in the image. The main color of the input image was extracted by 3D histogram and clustering algorithm, and the region growing algorithm was implemented to merge all the join points with the same color label to form the color region. Secondly, color classifier, shape classifier and relational classifier were defined based on the generated color region. Each classifier was used to calculate the retrieval advantage probability of the query image and the image in the database. Finally, through the decision-making combination process, the most similar trademarks were found according to the retrieval rules and the length of the list, and the dynamic selection scheme was used to further improve the system performance. Through the experiments, compared with current trademark retrieval schemes, the proposed retrieval system had more ideal Precision-Recall curve, which had higher robustness to scaling, distortion and noise. This algorithm has high retrieval accuracy under various geometric transformations, which has a good reference value for trademark registration, copyright protection and other industries.