At present, the logo recognition technology in China is being rapidly developed, which is embodied in processing accuracy, reproducibility, flexibility, applicability, and information compression. However, the development of this technology is still limited by actual demands. The deep learning model has heavy computation and is difficult to run on lightweight embedded devices. There are many and complex noises in industrial production, which affect the recognition accuracy. To solve the above problems, this study proposes a logo recognition technology based on the convolutional neural network. An improved Canny edge detection algorithm is used to enhance the robustness in edge information extraction, and signs are accurately extracted in a high-noise environment. In addition, to further improve the recognition accuracy, in the combination of Convolutional Neural Network (CNN) and ellipse fitting, this study combines the model recognition and ellipse fitting results to determine the recognition accuracy. This method improves the recognition accuracy while increasing a small amount of calculation.