本文对社交网络平台管理的过滤算法对集体意见的影响进行了建模和研究。提出了一种随机多主体意见动力学模型，该模型考虑了个体间相互作用强度的集中调整。每一种意见的演变都用马尔可夫链来描述，其转化率通过影响参数受到邻居意见的影响。在一般情况下以及在特殊情况下研究了该模型的性质。(In this paper,the effect on collective opinions of filtering algorithms managed by social network platforms is modeled and investigated. A stochastic multi-agent model for opinion dynamics is proposed, that accounts for a centralized tuning of the strength of interaction between individuals. The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected by the opinions of the neighbors through influence parameters. The properties of this model are studied in a general setting as well as in interesting special cases.)