Detecting　arrhythmia　through　electrocardiogram　(ECG)　signals　is　a　challenging　task,　and　some　methods　based　on　deep　neural　networks　have　been　proposed　to　assist　in　the　diagnosis　of　arrhythmia.　However,　the　performance　of　existing　models　in　obtaining　imbalanced　key　points　of　ECG　data　and　integrating　long-distance　information　needs　to　be　improved.　In　this　study,　we　propose　an　adaptive　arrhythmia　classification　network　(ADLNet)　based　on　deformable　convolution　and　bidirectional　LSTM.　This　network　uses　the　Bi-LSTM　gating　mechanism　to　obtain　and　control　the　cumulative　information　of　the　context,　enhance　the　ability　of　the　model　to　understand　the　context,　and　mine　the　long-term　sequential　information　in　the　lead.　Considering　the　imbalance　of　the　key　points　of　the　focus　of　ECG　and　the　gradient　disappearance　of　the　deep　network,　the　residual　deformable　convolution　module　with　deformable　receptive　field　is　introduced　to　better　obtain　the　local　features　of　ECG　and　enhance　the　ability　of　feature　representation.　In　addition,　the　adaptive　fusion　module　adaptively　combines　the　features　mined　from　the　first　two　with　different　weight　coefficients　to　improve　the　performance　of　the　model.　The　proposed　network　was　evaluated　on　the　12-lead　ECG　data　set　of　the　2018　China　Physiological　Signal　Challenge　2018,　and　the　F1　of　ADLNet　reached　0.861,　which　is　3%　higher　than　the　baseline　method.　The　experimental　results　show　that　the　performance　of　the　ADLNet　proposed　in　this　paper　is　better　than　that　of　the　mainstream　arrhythmia　classification　network.　This　study　has　demonstrated　that　the　adaptive　combination　of　deformable　convolutional　models　and　Bi-LSTM　models　can　achieve　excellent　ECG　classification　performance　without　the　assistance　of　other　medical　features.