Chinese and Western Art History Based on Big Data

Abstract

After entering the era of big data, artificial intelligence has developed rapidly, and the contradiction between technology and humanities has become increasingly prominent. World culture, technology, and art are all facing new conflicts and challenges. This article mainly studies the development and experience of Chinese and Western art history based on big data. This experiment uses an ordinary computer to build a Spark cluster environment by setting up 6 virtual hosts. Chinese artists are always more inclined to subjective creation, while the traditional Western realistic painting in the Renaissance period paid more attention to the three-dimensional painting color while achieving the harmony of the picture. Then sample the fingerprint at the reference point of the sparse density distribution to cluster the indoor area into a specific number of sub-regions, and use the sampling data of each sub-region to predict the fingerprints at other unmeasured reference points to reconstruct the complete Fingerprint database. The data shows that in the experimental cluster environment, when the processing data size is 50MB or far greater than 50MB, choosing parallel algorithms can save data processing time and improve data processing efficiency. The results show that both Chinese painting and Western modernist painting pursue the spontaneity, randomness and contingency of painting in terms of aesthetic effects. This is the common ground of the pursuit of expressiveness in Chinese and Western paintings.