ing at the known deficiencies with complex training, strict parameter-tuning skills and experiences, difficult theoretical analysis of deep neural networks, an improved image classification algorithm with high training efficiency, strong interpretability and simple theoretical analysis is proposed, in which the principal component analysis network (PCANet) is used for feature extraction and the flat neural network (FNN) is for classification. In addition, the model parameters can be obtained by direct calculation and the flat neural network adaptively determines the number of nodes according to the training dataset. When the nodes increase, it is not necessary to retrain the model and only the parameters need to be adjusted locally to update the model. The experimental results show that the proposed model can acquire rapid training. Moreover, it possesses more competition in recognition accuracy compared with other unsupervised classification algorithms and traditional deep neural networks.