The work aims to propose to design a trademark retrieval scheme based on Zernike moment coupled color space weighted measure with respect to the problem that it is difficult to fully characterize the trademark features, which can easily lead to poor retrieval accuracy and robustness during identification and measurement by the use of a single feature in the trademark retrieval system. Firstly, the Zernike moment was used as the shape descriptor of the trademark to fully describe the shape information of the trademark. Subsequently, a color space was used to describe the color distribution features of pixel spatial information in the image. Then, the Zernike moment feature and color space of the input trademark were matched with the features stored in the database to calculate the weighted Euclidean distance and the color space measure of the Zernike moment feature. Finally, combined with color space measure and Euclidean distance, the shape and color characteristics were comprehensively considered to form new distance measurement rules and output the trademark similar to the query trademark. The experiment data showed that, compared with the current trademark retrieval algorithm, the proposed algorithm had higher retrieval accuracy and robustness, indicating more ideal Precision-Recall and Mean Average Precision (MAP). The image retrieved by the proposed algorithm has a higher similarity to the query image, which has a certain reference value in the aspects of trademark registration and infringement protection, etc.