可视化与可视分析已成为众多领域中结合人类智能与机器智能协同理解、分析数据的常见手段。人工智能可以通过对大数据的学习分析提高数据质量，捕捉关键信息，并选取最有效的视觉呈现方式，从而使用户更快更准确更全面地从可视化中理解数据。此外，利用人工智能的方法，交互式可视化系统也能更好地学习用户习惯及用户意图，推荐符合用户需求的可视化形式、交互操作、数据特征，从而降低用户探索的学习及时间成本，提高交互分析的效率。近年来，人工智能方法在可视化中的应用受到了极大的关注，产生了大量学术成果。本文将从最新工作出发，探讨人工智能在可视化流程的关键步骤中的作用：如何智能地表示、管理数据？如何辅助用户快速创建、定制可视化？如何通过人工智能扩展交互手段、提高交互效率？如何借助人工智能辅助数据的交互分析？具体而言，本文将详细梳理每个步骤中需要完成的任务及解决思路，并介绍相应的人工智能方法（如深度网络结构）。而后，本文将以图表数据为例，介绍智能可视化与可视分析的应用。最后，本文将讨论智能可视化方法的发展趋势，并展望未来的研究方向及应用场景。

Visualization and visual analytics emerge as a common practice to analyze and understand data in the big data era. Visualization leverages visual encoding to transfer data into visual format, and visual analytics further involves user interaction through graphical interface to build a closed-cycle between users and data. These techniques allow users to build sophisticated data analysis workflow interactively and intuitively. However, the explosive growth of data raises great challenges to visual encoding and interactions: straightforward visual encoding becomes insufficient to represent the rich information of data in the limited screen-space, and the trail-and-error interaction is unaffordable to verify various hypotheses and explore all possibilities. To tackle these challenges, artificial intelligence is introduced in visualization approaches to optimize the information transfer among data, visualization, and users. The recent advancement in machine intelligence provides unprecedented power to represent data, visual representations, and user intention in a unified space and approximate complex connections among them. This may enhance the performance of conventional visualization approaches, and, more importantly, pave pathways to endless research possibilities beyond traditional visualization tasks. For example, in visualization creation, machine intelligence can capture important data features and user intentions through learning, which facilitates auto-creation of visualization that meets specific design requirements. This reduces the expertise and effort required to generate desired visualization from data. In scientific visualization, intelligent approaches can effectively learn from a large amount of ensemble members and produce visualization of unseen members. This enables real-time exploration of ensemble data without prohibitive simulation. In interactive techniques, machine learning can enrich interaction modalities to reduce learning and usage effort. One example is the natural language interface, which allows users to interact with visualization systems using natural language. In visual analytic systems, machine intelligence may learn users’ exploration behavior and suggest likely interaction operations. This reduces the trial-and-error effort and improve the interaction efficiency. This paper surveys the AI-based visualization techniques and discusses the trends and research opportunities. Particularly, we explore the role of AI in the key steps of the visualization pipeline: data representation, visualization creation, interactive exploration, and visual analytics. For data representation, we discuss how to leverage the power of AI for big data management and representation and support astonishing rendering. For visualization creation, we discuss how AI may facilitate effective transformation from data to its visual form preserving the key structures and highlighting interesting facts. For interactive exploration, we discuss how AI may be used to interpret user intention behind various kinds of interaction and how visualization systems response to the intention. For visual analytics, we focus on how AI may support the downstream analysis tasks. Additionally, we introduce the application of intelligent visualization and visual analytics with graphical data as an example. Finally, we will discuss the recent trends in intelligent visualization approaches and points out future research directions and application scenarios.