Biomedical　Question　Answering　aims　to　obtain　an　answer　to　the　given　question　from　the　biomedical　domain.　Due　to　its　high　requirement　of　biomedical　domain　knowledge,　it　is　difficult　for　the　model　to　learn　domain　knowledge　from　limited　training　data.　We　propose　a　contextual　embedding　method　that　combines　open-domain　QA　model　AoA　Reader　and　BioBERT　model　pre-Trained　on　biomedical　domain　data.　We　adopt　unsupervised　pre-Training　on　large　biomedical　corpus　and　supervised　fine-Tuning　on　biomedical　question　answering　dataset.　Additionally,　we　adopt　an　MLP-based　model　weighting　layer　to　automatically　exploit　the　advantages　of　two　models　to　provide　the　correct　answer.　The　public　dataset　biomrc　constructed　from　PubMed　corpus　is　used　to　evaluate　our　method.　Experimental　results　show　that　our　model　outperforms　state-of-The-Art　system　by　a　large　margin.　©　2022　ACM.