How to quickly and effectively mine valuable information from massive data to better guide decision-making is an important goal of big data analysis. Visual analysis is an important big data analysis method, and it takes advantage of the characteristics of human visual perception, utilizes visualization charts to present laws contained in complex data intuitively, and supports human-centered interactive data analysis. However, the visual analysis still faces several challenges, such as the high cost of data preparation, high latency of interaction response, high threshold for visual analysis, and low efficiency of interaction modes. To address the above challenges, researchers propose a series of methods to optimize the human-computer interaction mode of visual analysis systems and improve the intelligence of the system by leveraging data management and artificial intelligence techniques. This study systematically sorts out, analyzes, and summarizes these methods and puts forward the basic concept and key technical framework of intelligent data visualization analysis. Then, under the framework, the research progress of data preparation for visual analysis, intelligent data visualization, efficient visual analysis, and intelligent visual analysis interfaces both in China and abroad is reviewed and analyzed. Finally, this study looks forward to the future development trend of intelligent data visualization analysis.