1. A method for training a Convolutional Neural Network (CNN) model, comprising: acquiring, by a server, initial model parameters of a CNN model to be trained, the initial model parameters comprising initial convolution kernels and initial bias matrixes of convolution layers of respective levels, and an initial weight matrix and an initial bias vector of a fully connected layer; acquiring a plurality of training images; on the convolution layer of each level, performing, by the server, convolution operation and maximal pooling operation on each of the training images to obtain a first feature image of each of the training images on the convolution layer of each level by using the initial convolution kernel and initial bias matrix of the convolution layer of each level; performing, by the server, horizontal pooling operation on the first feature image of each of the training images on the convolution layer of at least one of the levels to obtain a second feature image of each of the training images on the convolution layer of each level; determining, by the server, a feature vector of each of the training images according to the second feature image of each of the training images on the convolution layer of each level; processing, by the server, each feature vector to obtain a classification probability vector of each of the training images according to the initial weight matrixes and the initial bias vectors; calculating, by the server, a classification error according to the classification probability vector and initial classification of each of the training images; regulating, by the server, the model parameters of the CNN model to be trained on the basis of the classification errors; on the basis of the regulated model parameters and the plurality of training images, continuing, by the server, the process of regulating the model parameters, until the number of iterations reaches a preset number; and determining, by the server, model parameters obtained when the number of iterations reaches the preset number as the model parameters of the trained CNN model.