Research on Demand Preference Analysis System of Contemporary Art Market Based on Big Data

Abstract

In less than 30 years, the Chinese art market has completed a gorgeous transformation from traditional to modern. In the process of development, great changes have taken place in the form of China's art market. The scale of art transaction is gradually expanding, and the price of Chinese art market is also getting higher and higher. In order to better achieve efficient retrieval under the background of big data and know the demand of contemporary art market, this paper studies a kind of art demand preference analysis system based on big data. Based on the big data environment, using machine learning method, more accurate detection of the general public's interest in art. By extracting user preferences from the big data of users' search records, a search model with synchronous changes with user preferences is established by using machine learning method, so as to predict users' interest preference for artworks in advance. The system is applied to Baidu, Jingdong and other platforms, and a questionnaire survey is carried out. The results show that the detected data are similar to the survey results.