针对随机子空间模态识别过程因人为参与造成识别结果不准确的问题，提出在随机子空间识别过程中引入谱系聚类算法实现模态参数自动识别。通过两种不同状态矩阵计算方法所得到的模态之间的相似程度以及各阶模态的能量剔除由噪声、模态过估计等因素引起的虚假模态；引入谱系聚类算法对结果进行拾取，以频率、阻尼比、模态振型、模态能量作为聚类因子计算结果之间的相似性，通过其将计算结果分为若干类，提取元素多于一定值的类作为拾取结果，实现结果的自动拾取。通过数值仿真和实例分析验证本文方法的有效性。

Due to the influence by human factors on the stochastic subspace identification results, hierarchical clustering algorithm is introduced to overcome this shortcoming. spurious modes resulting from noise or model redundancy can be removed according their mode energy or mode similar index which describes the similarity between modes obtained by two different algorithm; the hierarchical clustering method is introduced to pick up the system modes, the results can be classified into several categories according the similarity between the results which can be calculated by eigenfrequencies, damping ratios, mode shapes and mode energy, some categories will be selected if the number of its elements is large enough. A numerical example and an experimental example are presented to demonstrate the efficacy of the method.