In order to solve a series of software development and operation and maintenance problems, such as maintainability andscalability,which may be faced?by software systems with increasing functions, scale and complexity, microservice extraction is a hotproblem currently. The existing work of microservice extraction is mainly to divide the monolithic program into potential microservicecandidates through the clustering of microservices. The graph convolutional network ( GCN) for automatic microservice extraction hasbeen obtained potential results,but being lack of taking full advantage of multi - channel information. To solve the above problems,amonolith decomposition method using deep learning clustering based on multi-channel attention map neural network ( MAGEMP) is proposed. Multiple independent graph convolutional networks are used to learn different interactions between topological graph and attributegraph nodes of different modal, and then the fusion of different embedding representations is further obtained through the attentionmechanism,finally the joint learning framework integrating clustering information obtains high - quality microservice division. Thevalidity of the model is verified from multiple angles on four public data sets. Compared with similar methods,MAGEMP improves thelearning ability of embedded features,and significantly improves the performance of testing on the open data set of monomer programs,such as functionality and modularity.