Spectral clustering is a flexible and effective clustering method for complex structure data sets. It is based on spectral graph theory and can produce satisfactory clustering results by mapping the data points into a low-dimensional space constituted by eigenvectors so that the data structure is optimized. But in the process of spectral clustering, the computational complexity of eigen-decomposition is usually O(n3), which limits the application of spectral clustering algorithm in big data problems. Nyström extension method uses partial points sampled from the data set and approximate calculation to simulate the real eigenspace. In this way, the computational complexity can be effectively reduced, which provides a new idea for big data spectral clustering algorithm. The selection of sampling strategy is essential for Nyström extension technology. In this paper, the design of an adaptive Nyström sampling method is presented. The sampling probability of every data point will be updated after each sampling pass, and a proof is given that the sampling error will decrease exponentially with the increase of sample times. Based on the adaptive Nyström sampling method, a spectral clustering algorithm for big data analysis is presented, and its feasibility and effectiveness is verified by experiments.%面对结构复杂的数据集，谱聚类是一种灵活而有效的聚类方法，它基于谱图理论，通过将数据点映射到一个由特征向量构成的低维空间，优化数据的结构，得到令人满意的聚类结果.但在谱聚类的过程中，特征分解的计算复杂度通常为O(n3)，限制了谱聚类算法在大数据中的应用.Nyström扩展方法利用数据集中的部分抽样点，进行近似计算，逼近真实的特征空间，可以有效降低计算复杂度，为大数据谱聚类算法提供了新思路.抽样策略的选择对Nyström 扩展技术至关重要，设计了一种自适应的 Nyström 采样方法，每个数据点的抽样概率都会在一次采样完成后及时更新，而且从理论上证明了抽样误差会随着采样次数的增加呈指数下降.基于自适应的Nyström采样方法，提出一种适用于大数据的谱聚类算法，并对该算法的可行性和有效性进行了实验验证.
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