Biomedical　semantic　question　answering　refers　to　answering　questions　from　a　given　contextual　passage　in　the　biomedical　field.　The　traditional　methods　need　complicated　feature　engineering　and　the　end-to-end　neural　network　models　depend　on　the　large　scale　dataset.　We　propose　a　hierarchical　multi-layer　transfer　learning　model　to　address　the　question　answering　task　on　the　biomedical　data　which　lacks　of　sufficient　training　data.　The　domain　adaptation　techniques　are　adopted　to　reinforce　the　performance,　include　fine-tuning　and　forgetting　cost　regularization　which　penalize　the　deviations　from　the　source　model＇s　parameters　and　avoid　forgetting　the　knowledge　of　source　domain.　The　distributed　representation　of　the　word　is　generated　and　domain　knowledge　of　biomedical　word　embedding　is　integrated.　Especially,　the　co-attention　mechanism　captures　the　question　interaction　clues　for　passage　encoding.　The　open　data　set　of　2017　BioASQ-Task　5B　is　used　to　evaluate　the　system　performance.　The　results　show　that　the　domain　adaptation　techniques　make　the　system　get　the　state-of-the-art　performance.　Our　model　without　any　handcrafted　feature　achieves　higher　precision　than　the　best　solution　for　factoid　question　in　2017　BioASQ-Task　5B.