在对等网络中,节点可以动态地进入和离开网络,增加了业务监测的难度。该文提出了采用主成分分析(PCA)方法检测并分析对等网络流量特征,解释了流量协方差矩阵最大特征值和最大特征矢量的物理意义,同时定义权重矢量作为流量观测指标,利用流量的协方差矩阵最大特征值和特征向量给出了全网的P2P流量时间和空间的动态特性。仿真结果表明,该方法能够很好地识别P2P流量的动态时间和空间特性,为互联网流量检测提供了一种有效方法。 In peer-to-peer networks, nodes can dynamically enter and leave the network, increasing the difficulty of service monitoring. In this paper, principal component analysis (PCA) is proposed to detect and analyze the characteristics of P2P traffic, and the physical meaning of maximum eigenvalue and maximum eigenvector of traffic covariance matrix is ​​explained. At the same time, weight vector is defined as traffic observation index, The maximum eigenvalue and eigenvector of covariance matrix give the dynamic characteristics of time and space of P2P traffic in the whole network. Simulation results show that this method can identify the dynamic time and space characteristics of P2P traffic well and provide an effective method for Internet traffic detection.