Existing epitope prediction methods have poor performance on overlapping epitope prediction of antigen. In order to slove the problem， a novel epitope prediction model with the overlapping subgraph mining algorithm based on Local Metric （L-Metric） was proposed. Firstly， an atom graph was constructed based on surface atoms of antigen and upgraded to an amino acid residue graph subsequently. Then， the amino acid residue graph was divided into non-overlapping seed subgraphs by the information flow based graph partitioning algorithm， and these seed subgraphs were expanded to obtain overlapping subgraphs by using the L-Metric based overlapping subgraph mining algorithm. Finally， these expanded graphs were classified into epitopes and non-epitopes by using a classification model constructed based on Graph Convolutional Network （GCN） and Fully Connected Network （FCN）. Experimental results show that， the F_{\mathrm{1}} -score of the proposed model is increased by 267.3%， 57.0%， 65.4% and 3.5% compared to those of the existing epitope prediction models such as Discontinuous epiTope prediction 2 （DiscoTope 2）， Ellipsoid and Protrusion （ElliPro）， Epitope Prediction server （EpiPred） and overlapping Graph cLustering-based B-cell epitope predictor （Glep） respectively in the same dataset. At the same time， the ablation experimental results show that the proposed overlapping subgraph mining algorithm can improve the prediction performance effectively， and the model with the proposed algorithm has the F_{\mathrm{1}} -score increased by 19.2% compared to the model without the proposed algorithm.