Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization

In recent years, advancements in data analysis techniques and deep learning algorithms have revolutionized the field of art cultural studies. Ancient Chinese stone carving decoration holds significant historical value, reflecting artistic stylistic evolution different periods. This paper explored Weighted Long Short-Term Memory Deep Learning (WLSTM – DL) characteristics ancient through application image visualization combined with a (LSTM) time-series architecture. The WLSTM-DL model uses optimized feature selection grasshopper optimization for extraction selection. By analyzing comprehensive dataset images from periods, captures temporal relationships patterns decoration. utilizes LSTM, specialized deep-learning architecture data, to uncover identify changes over time. findings this study provide valuable insights into development showcase potential uncovering hidden narratives understanding intricate details artworks.