Research on Improved Algorithm of Image Classification Based on Convolutional Neural Network

Abstract

With the continuous exploration of artificial intelligence, the convolutional neural network, as one of the representative algorithms, has also developed rapidly. The convolutional neural network extracts more high-dimensional and abstract features from the data, summarizes the distributed feature representation of the data, and discovers complex nonlinear relationships. Due to the rapid increase in the amount of calculations in the era of big data, the structure of convolutional neural networks is also more complex, so the difficulty of computing tasks continues to increase. Aiming at these difficulties, this paper optimizes the convolutional neural network model AlexNet. This paper first introduces the basic principles of artificial neural networks and related technologies of convolutional neural networks, and analyzes the development prospects and research directions of convolutional neural network algorithms. Then introduce the convolutional neural network model AlexNet, and analyze and summarize its shortcomings.