This　paper　is　concerned　with　the　task　of　biomedical　Question　Answering　(QA)　which　refers　to　extracting　an　answer　to　the　given　question　from　a　biomedical　context.　Current　works　have　made　progress　on　this　task,　but　they　are　still　severely　restricted　by　the　insufficient　training　data　due　to　the　domain-specific　nature,　which　motivates　us　to　further　explore　a　powerful　way　to　solve　this　problem.　We　propose　a　Hierarchical　Question-Aware　Context　Learning　(HQACL)　model　for　the　biomedical　QA　task　constituted　by　multi-level　attention.　The　interaction　between　the　question　and　the　context　can　be　captured　layer　by　layer,　with　multi-grained　embeddings　to　strengthen　the　ability　of　the　language　representation.　A　special　training　method　called　DA,　including　two　parts　namely　domain　adaptation　and　data　augmentation,　is　also　introduced　to　enhance　the　model　performance.　Domain　adaptation　can　be　defined　as　pre-training　on　a　large-scale　open-domain　dataset　and　fine-tuning　on　the　small　training　set　of　the　target　domain.　As　for　the　data　augmentation,　the　Round-trip　translation　method　is　adopted　to　create　new　data　with　various　expressions,　which　almost　doubles　the　training　set.　The　public　biomedical　dataset　collected　from　PubMed　provided　by　BioASQ　is　used　to　evaluate　our　model.　The　results　show　that　our　approach　is　superior　to　the　best　recent　solution　and　achieves　a　new　state　of　the　art.