The　input-output　relationship　of　inductive　displacement　sensors　is　typically　nonlinear,　which　demands　numerous　computational　resources　for　precise　calculation.　Therefore,　the　traditional　analytical　methods　for　the　inductive　displacement　sensors　cannot　meet　the　real-time　high-precision　measurement　requirements.　In　response　to　the　aforementioned　issues,　this　paper　proposes　an　optimized　Long　Short-Term　Memory　(LSTM)　neural　networks　based　on　one-dimensional　convolutional　neural　networks　(1DCNN)　to　modeling　the　input-output　relationship　of　the　inductive　displacement　sensor.　First,　the　measurement　principle　of　the　inductive　displacement　sensors　was　analyzed,　and　the　analytical　model　of　the　sensor　output　was　derived.　And　the　influences　of　the　key　parameters　of　sensors　on　the　relationship　between　the　induced　voltage　and　the　displacement　were　studied.　Then,　the　1DCNN-LSTM-AT　network　for　modelling　the　input-output　relationship　of　the　inductive　displacement　sensor　was　studied.　The　spatial　features　of　historical　induced　electromotive　force　(EMF)　data　generated　by　the　induction　coil　were　initially　extracted　using　the　1DCNN　network.　And　these　spatial　features　were　then　utilized　as　input　to　the　LSTM　neural　network　to　capture　the　temporal　features　of　the　historical　induced　EMF　data.　Subsequently,　the　spatiotemporal　features　of　the　induced　EMF　data　were　fed　into　the　regression　prediction　layer　to　compute　the　displacement　measurement　results　corresponding　to　the　current　input.　Moreover,　the　attention　mechanism　was　used　for　the　1DCNN-LSTM　model　to　enhance　the　prediction　accuracy　and　stability　of　the　model.　Finally,　the　experimental　results　demonstrate　that　the　proposed　1DCNN-LSTM-AT　model　achieves　an　average　absolute　percentage　error　(MAPE)　of　3.1%,　significantly　outperforming　traditional　models　such　as　LSTM　(29.3%),　CNN　(4.7%),　and　ANN　(4.3%).　This　paper　provides　a　new　method　for　modelling　the　nonlinear　relationships　of　the　inductive　displacement　sensor,　and　presents　a　fresh　perspective　for　research　in　the　data　processing　of　nonlinear　sensors.