Abstract: Community discovery aims to uncover the community structure embedded in complex networks, which is one of the important tasks in complex network analysis. However, most of the existing community discovery methods are aimed at single-layer network data, and less research has been done on the widespread multi-layer networks in the real world. In order to solve the problem of community discovery in multi-layer networks, we propose a two-stage ensemble-based community discovery algorithm. The algorithm can improve the accuracy and interpretability of community discovery results. Firstly, after the base communities are obtained at each layer, the local ensemble is performed based on the base community structure information of each layer and the optimal base community structure of each other layer. Secondly, the stability of each community in the local community division of each layer is measured based on information entropy, and the accuracy of each local community division is evaluated through the results of other layer community division. Finally, a global weighted ensemble is carried out based on the importance of each community and the community structure to obtain the final community structure. A comparative analysis is carried out on artificial multi-layer networks and real multi-layer networks with existing multi-layer network community discovery algorithms. The experimental results show that our proposed algorithm is superior to the existing algorithms in terms of multi-layer modularity and normalized mutual information.