The invention relates to a distributed soft clustering method in an Internet-of-Things environment based on an average
consensus algorithm, and the method comprises the following specific steps: S1, obtaining a topological network where a target Internet-of-Things node is located, and inputting a distributed
data set, a clustering number, a fuzzy coefficient and a stop criterion parameter into thetopological network; S2, initializing set elements of the distributed
data set, and calculating the initial clustering center of the target Internet-of-Things node; S3, calculating a
distribution matrix from the distributed
data set to the initial clustering center; S4, calculating a clustering center in the target Internet-of-Things according to the
distribution matrix, and obtaining a global clustering center through an average
consensus algorithm; and S5, repeating the steps S1-S4, iteratively updating the global clustering center, judging the current global clustering center and the global clustering center of the previous round according to the stop criterion parameters, and outputting a final global clustering center. Compared with the prior art, the method has the advantages that the clustering result quality and the
algorithm stability can be effectively improved, and the like.