Aiming at low efficiency of the existing noise image classification,a noise image classification algorithm based on the improved Darknet is proposed.The 1×1 convolution layer in the output part of Darknet network is removed,the number of convolution kernels in Layer 19 is changed to four,and Softmax layer is added to the end of the network,so that the classification function of the network is realized.Dropout is introduced after the passthrough layer and after Layer 6,Layer 7 and Layer 8 respectively,and L2 regularization is introduced in the convolution layer,so as to avoid network over-fitting.Layer 10 and 11,Layer 12 and 13,Layer 15 and 16,Layer 17 and 18 of the network are changed into four residual blocks to avoid gradient disappearance when updating the weights in back propagation.20000 images are taken from CIFAR-10 data set,and four kinds of noise,that is,Gaussian,salt,speckle and Poisson,are added respectively after 128×128 size transformation.One-hot coding is carried out for each image according to its category.Finally, the images and labels are made into a training set,a verification set and a test set.The experimental results of the four algorithms show that the accuracy of the improved Darknet network for color noise image classification can reach 0.904,which is much higher than that of the other three algorithms.