Automatic recognition and generation algorithm for design art style based on deep learning

With the rapid growth of deep learning (DL) technology, automatic recognition and artistic processing of design styles have become a research hotspot in the fields of computer vision (CV) and artificial intelligence (AI). Image style transfer technology involves artistic processing of the target image, which not only preserves the content features of the original image, but also endows it with a specific artistic style. This article proposes an innovative algorithm based on DL, which integrates automatic recognition and generation of design art styles. Specifically, we first utilized Convolutional Neural Networks (CNN) to construct an efficient automatic recognition model for design art styles, which can accurately identify the features of multiple art styles. Subsequently, we combined generative adversarial networks (GANs) to construct a style transfer framework, achieving artistic style transformation of the target image. The results show that compared with existing methods, our algorithm has significantly improved the accuracy of art style recognition, and the generated stylized images have significantly improved precision.