卷积神经网络是近年发展起来，并引起广泛重视的一种高效识别方法。20世纪60年代，Hubel和Wiesel在研究猫脑皮层中用于局部敏感和方向选择的神经元时发现其独特的网络结构可以有效地降低反馈神经网络的复杂性，继而提出了卷积神经网络（Convolutional Neural Networks-简称CNN）。现在，CNN已经成为众多科学领域的研究热点之一，特别是在模式分类领域，由于该网络避免了对图像的复杂前期预处理，可以直接输入原始图像，因而得到了更为广泛的应用。(Convolution neural network (convolution neural network) is an efficient recognition method which has been developed in recent years and has attracted much attention. In 1960s, Hubel and Wiesel are used to select local sensitive and the direction of the neurons in the cerebral cortex of the cat is found in its unique network structure can effectively reduce the complexity of the feedback neural network, and then puts forward the convolutional neural network (Convolutional Neural Networks- CNN). Nowadays, CNN has become one of the research hotspots in many fields of science. Especially in the field of pattern classification, because the network avoids the complex preprocessing of images, it can directly input original images, so it has been widely applied.)