Overlapping community discovery is an important basic work in complex network mining.It can be applied to the data analysis of social networks,communication networks,protein interaction networks,metabolic path networks,transportation networks and other networks,so as to serve the fields of intelligent transportation,infectious disease prevention and control,public opinion analysis,new drug development and human resource management.The traditional stand-alone computing architecture has been difficult to meet the analysis and computing requirements of various large-scale complex networks.Researchers in the field of artificial intelligence propose to apply community discovery to the field of network representation learning to enrich the characteristics of nodes and edges in the network.However,the traditional overlapping community discovery algorithm fails to consider the relevant requirements from the network representation learning task in its design,only focuses on the community division of nodes,and lacks consideration of the internal structure and external boundary of the community.For example,it does not involve the weight of nodes within the community and the attribution ranking belonging to multiple communities,so it cannot provide richer characteristic information of nodes and communities in the network,resulting in insufficient support for network representation learning tasks. Aiming at the problem that the traditional single machine overlapping community discovery algorithm is not suitable for large-scale complex network mining and cannot support the relevant requirements of network representation learning tasks,a distributed overlapping community discovery algorithm based on community forest model (DCFM algorithm) was proposed.Firstly,the network dataset was stored in the distributed file system,the data were divided into blocks,and the distributed computing framework was used to execute the CFM algorithm on each data block;then,the community consolidation was performed;Finally,the community division results were summarized,and the algorithm was run on the spark cluster by using the real DBLP dataset and was evaluated by F Value and running time.The results show that the f-means of DCFM algorithm is slightly inferior to that of CFM algorithm,but its operation time decreases linearly with the increase of nodes.While sacrificing a small part of f-means,DCFM algorithm has the ability to process large-scale network data;the number of split copies has a great impact on the calculation time,which can be found in com DBLP ungraph.Txt data set,CFM algorithm needs 192 min to process data,while DCFM algorithm needs about 91 min to divide the data into 6 parts,and only about 13 min after dividing into 100 parts.Therefore,on the big data platform,DCFM algorithm uses distributed computing backbone to divide and merge communities,which is a feasible large-scale complex network mining method.By dividing the network,it can greatly improve the speed of community division and the efficiency of community discovery.