For　insulated　gate　bipolar　transistor　(IGBT)　modules　using　wire　bonding　as　the　interconnection　method,　the　main　failure　mechanism　is　cracking　of　the　bonded　interface.　Studying　the　mechanical　properties　of　the　bonded　interface　is　crucial　for　assessing　the　reliability　of　IGBT　modules.　In　this　paper,　first,　shear　tests　are　conducted　on　the　bonded　interface　to　test　the　bonded　interface＇s　strength.　Then,　finite　element-cohesive　zone　modeling　(FE-CZM)　is　established　to　describe　the　mechanical　behavior　of　the　bonded　interface.　A　novel　machine　learning　(ML)　architecture　integrating　a　convolutional　neural　network　(CNN)　and　a　long　short-term　memory　(LSTM)　network　is　used　to　identify　the　shape　and　parameters　of　the　traction　separation　law　(TSL)　of　the　FE-CZM　model　accurately　and　efficiently.　The　CNN-LSTM　architecture　not　only　has　excellent　feature　extraction　and　sequence-data-processing　abilities　but　can　also　effectively　address　the　long-term　dependency　problem.　A　total　of　1800　sets　of　datasets　are　obtained　based　on　numerical　computations,　and　the　CNN-LSTM　architecture　is　trained　with　load-displacement　(F-delta)　curves　as　input　parameters　and　TSL　shapes　and　parameters　as　output　parameters.　The　results　show　that　the　error　rate　of　the　model　for　TSL　shape　prediction　is　only　0.186%.　The　performance　metric＇s　mean　absolute　percentage　error　(MAPE)　is　less　than　3.5044%　for　all　the　predictions　of　the　TSL　parameters.　Compared　with　separate　CNN　and　LSTM　architectures,　the　proposed　CNN-LSTM-architecture　approach　exhibits　obvious　advantages　in　recognizing　TSL　shapes　and　parameters.　A　combination　of　the　FE-CZM　and　ML　methods　in　this　paper　provides　a　promising　and　effective　solution　for　identifying　the　mechanical　parameters　of　the　bonded　interfaces　of　IGBT　modules.