为了保障感知服务过程中能够建立一个持久稳定的任务分发链路，文中首先提出了基于节点社会属性的相似度量化算法（Intimacy Quantification Method Based on Social Attributes,IQSA）；然后结合信息熵理论与社会关系提出了一种基于信息熵相似度的社区检测算法（Community Detection Algorighm Based on Information Entropy Similarity,CDIES）；并通过实验对IQSA算法与当前比较流行的两种模型进行了比较，从最终的社区划分结果、模块度和时间开销三个方面，评估分析了CDIES算法的准确性和有效性。结果表明：与基于内容的好友推荐模型和基于关系的两阶段好友推荐模型相比，IQSA算法在准确率、召回率与 f1-score 上的综合表现最优；CDIES算法的社区划分结果的模块度值和时间开销明显优于GN算法和FN算法。

In order to establish a permanent and stable task distribution link in the perceptual service process, firstly, an intimacy quantification method based on social attributes(IQSA) was proposed; secondly, a community detection algorighm based on information entropy similarity（CDIES）was proposed by combining the information entropy theory with social relationship; finally, IQSA algorithm was compared with two popular models by experiments, and the accuracy and validity of CDIES algorithm was assessed according to the result of community devision, modularity and time cost. The experimental results show that, compared with the content-based friend recommendation and relationship-based two typical recommendation algorithms, the IQSA algorithm has best comprehensive performance in accuracy, recall, and f1-score. And the modularity and time cost of the result of community devision of CDIES algorithm outperforms that of GN algorithm and FN algorithm.