Algorithm design and application research of English picture recognition intelligent system based on convolutional neural network

With the development of technology and the advancement of educational concepts, the country has put forward new requirements for the training model in the context of the era of artificial intelligence. The advancement of science and technology has made big data and artificial intelligence the focus of exploration and research of the times. Among them, the deep learning network in the field of artificial intelligence can solve many complex pattern recognition problems by simulating the human brain, but there are still shortcomings. Most existing deep networks have complex structures, involve a large number of hyperparameters, and suffer from extremely time-consuming training processes. This research introduces the basic knowledge of augmented reality technology and artificial intelligence into the English teaching system, and explores the deep integration of science education and artificial intelligence based on augmented reality technology. A wide learning system model based on convolutional neural network is proposed. First, the image is extracted through the convolutional layer and the pooling layer of the convolutional neural network to obtain the extracted features. In order to extract the features more accurately, the Adam algorithm is used to iteratively update the parameters of the convolutional layer and the pooling layer for a certain number of times. Then, the extracted image features are input into the width learning system, and feature nodes and enhancement nodes are established. Using the SqueezeNet design principle and the SVD algorithm to compress the proposed network as a whole, a lightweight network is finally obtained.