Research on Improving Image Classification Technology Based on Deep Convolutional Neural Network Model

In this paper, the core of image classification and recognition technology, i.e. the principle of convolution operation and image feature extraction, is combed in detail, and the storage form of images in the computer is shown. On this basis, the basic principle and method of image feature extraction with the help of deep convolutional neural network is analyzed, and the programming is implemented. The author verified the classification effect of the established model by using 3000 image data. The experimental results show that the application effect of image classification technology (image recognition speed and accuracy) is closely related to the quality and learning rate of the deep convolutional neural network model.