Application of Woodblock New Year Pictures Image Processing Based on Deep Learning and Feature Extraction

This paper presents a method for processing woodblock new year pictures based on deep learning and feature extraction. This method uses convolutional neural networks (CNN) to perform feature extraction and image classification, and uses multiple convolutional layers to obtain the main features of the New Year picture images, such as texture, color, and structure, to achieve efficient image recognition and processing. First, a convolutional neural network model specifically for the image features of woodblock new year pictures was constructed; secondly, the accuracy and robustness of the proposed method were verified through model simulation. In the experimental phase, thousands of woodblock new year pictures were selected for data training and testing, and compared with traditional methods. The simulation results show that the proposed method has significantly improved the accuracy and speed of feature extraction and image processing, with an accuracy rate of more than 95% and a processing speed increase of about 30%. The research in this paper provides effective technical support for the digital protection of woodblock new year pictures, and provides a new direction for the intelligent processing of other types of traditional artworks.