Efficiently solving sparse linear algebraic equations is an important research topic of numerical simulation. Commonly used approaches include direct methods and iterative methods. Compared with the direct methods, the iterative methods have lower computational complexity and memory consumption, and are thus often used to solve large-scale sparse linear equations. However, there are numerous iterative methods, parameters and components needed to be carefully chosen, and an inappropriate combination may eventually lead to an inefficient solution process in practice. With the development of deep learning, intelligent iterative methods become popular in these years, which can intelligently make a sufficiently good combination, optimize the parameters and components in accordance with the properties of the input matrix. This survey then reviews these intelligent iterative methods. To be clearer, we shall divide our discussion into three aspects: a method aspect, a component aspect and a parameter aspect. Moreover, we summarize the existing work and propose potential research directions that may deserve a deep investigation.

翻译：高效求解稀疏线性代数方程是数值模拟的重要研究方向，常用方法包括直接法和迭代法。与直接法相比，迭代法计算复杂度和内存消耗更低，因此常被用于求解大规模稀疏线性方程组。然而，迭代方法种类繁多，其参数与组件均需精心选择，实际应用中不当的组合可能导致求解效率低下。随着深度学习的发展，智能迭代方法近年来备受关注，这类方法能够根据输入矩阵的特性智能地选择最优组合，优化参数与组件。本文综述了这些智能迭代方法，为清晰起见，将讨论分为方法层面、组件层面和参数层面三个维度。同时，我们总结了现有研究成果，并提出了值得深入探索的潜在研究方向。