Analysis of Style Conversion Mechanism of Art Works Assisted by Artificial Intelligence

The diversity of art styles provides a vast visual expression space for artistic creation. In this context, the study of the mechanism of style transformation in art works is particularly important. It not only broadens the boundaries of artistic creation, but also brings unprecedented visual experiences to the audience. The core of this mechanism lies in the application of deep learning (DL) algorithms, especially style transfer techniques. Style transfer technology, as a model of the integration of artificial intelligence (AI) and art, can cleverly apply the artistic style from one image to another, thereby generating art works that retain the original image content while incorporating new styles. This article delves into the mechanism of art style transfer assisted by AI and proposes an innovative DL based style transfer algorithm. This algorithm effectively improves the efficiency and quality of style transfer by optimizing the network structure and learning strategy, enabling the converted work to present the characteristics of the target style more realistically while preserving the details of the original image. The experimental results show that the algorithm can achieve a relatively ideal style transfer effect.