Abstract: For image classification processing， convolutional neural network （CNN） is a common method. But the current methods based on CNN construction do not make full use of the perceptual characteristics of visual neurons， so that the network loses a lot of important image feature information in the process of learning. Therefore， starting from the perceptual characteristics of visual neurons， this paper proposes a deep feedback convolutional neural network model that conforms to visual perception. In this model， the feedback regulation mechanism of visual neurons is simulated， and the deep feedback recurrent neural network （DF-RNN） is constructed. At the same time， combining the advantages of DF-RNN and CNN， DF-RNN is embedded in CNN to exert its associative memory function， and then deep features are extracted from shallow features through DF-RNN. In addition， because the weight parameters of DF-RNN adopt a sharing mechanism， the number of parameters for network training is greatly reduced. Finally， the image classification experiment on the Oxford flowers-102 standard dataset is carried out by the network model， and the classification accuracy can reach 86.8%， which is 9.6 percentage points higher than VGG16. It shows the effectiveness of the proposed network model.

Key words: image classification, CNN, visual neurons, associative memory