稀疏线性方程组的高效求解是数值计算方向的研究热点之一，其中包括预处理技术的研究。本文从技术分类的角度，总结了稀疏线性方程组求解中的预处理技术。首先，介绍了填充元缩减策略，旨在减少求解过程中存储量的同时，仍能保持矩阵的稀疏结构；其次，介绍了不同结构系数矩阵的多种匹配技术，旨在获得矩阵的对角优势性；最后，介绍了具有天然并行性的因子分解近似逆预条件子构造方法和不完全分解预条件中的并行求解技术等。

The effective solution to sparse linear systems which is one of the hotpots in numerical computation focuses also on the preprocessing arts for the systems. From the view of classification to the techniques,this paper sums up the preprocessing methods. Concretely,it introduces the fillreducing strategies to decrease the storage and keep the matrices sparse in the solving process,the matching strategies for  different structural kinds of matrices to obtain  different diagonal dominances, the sparse approximate inverse proconditioner holding the parallel characteristics in nature,and the parallel methods for incomplete factorization,and so on.