Image recognition technology and applications based on CNN

This paper studies image recognition technology based on Convolutional Neural Networks (CNN) and explores its application on the CIFAR-10 dataset. By building a CNN model and integrating data preprocessing, network design, and parameter optimization, experimental results show that CNN outperforms traditional methods in image classification tasks, achieving an accuracy of 92.3%. Precision, recall, and F1-score demonstrate a well-balanced performance, confirming the advantages of CNN in complex image recognition tasks. This research provides theoretical support for the application of CNN in image classification and offers directions for future network optimization and practical applications.