An improved image classification method based on an impulse deep neural network is provided. A DOG layer and a simplified pulse couple neural network are used to preprocess an image, a gray-scale image is generated through the DOG layer to generate a contrast map, and the simplified impulse coupled neural network processes the contrast image generated by DOG layer by parameter adaptive method. According to the different content of the generated contrast image, the larger the pixel value is, the earlier the ignition time is, the impulse image with different number of channels is generated, thatis, the time series impulse image. The improved impulse depth neural network is trained by an STDP unsupervised algorithm. The weight matrix of a convolution layer is updated by STDP weight modification mechanism until the maximum number of iterations of the current convolution layer is reached, and then the training process of the next convolution layer is repeated, and the trained impulse depthneural network is obtained. The method has the advantages of being closer to biological characteristics, being simple and effective, and being suitable for image recognition of handwritten numerals,faces, other objects and the like.