The powerful learning ability of a convolutional neural network to perform functional classification provides valuable clues for the discovery of biological problems and image recognition. However, the mechanism of deep learning still lacks satisfying interpretation and the convolutional neural network is usually regarded as a black-box model. Consequently, it is challenging to design deep learning models and adjust the hyper-parameters for specific tasks without theoretical guidance. We raise a new task of deep learning – 2-dimensional logo detecting for computer vision and develop a novel model of convolutional neural networks to solve the task. Furthermore, we interpret this specific model from the point of view of statistical learning. With the guidance of statistical interpretation, we can design the model structure more efficiently and obtain better performances.