Big data analysis methods and cultural value mining for the historical research of ceramic art

Abstract

Chinese ceramic art occupies an important position in the world of ceramic art and is an important component of Chinese traditional culture. This study uses crawler technology to collect relevant text data of ceramic art history, then vectorize the text, extract and analyze the keywords of ceramic art history based on TF-IDF and word co-occurrence model, and then combine with the text sentiment analysis model based on Seq2seq model to realize the value mining of ceramic art history and culture. Ceramic art in history is mostly characterized by artistic diversification, design diversification, appearance design, and situational integration with rich colors. China (818 frequency), development (781 frequency), and culture (752 frequency) are the most frequently used keywords. The history of ceramic art is closely united with the traditional Chinese culture in its development, and at the same time, the culture of ceramic craft also has a high technical content, reflecting the aesthetic value and technical value of the history and culture of ceramic art.