Optimizing　the　input　parameters　for　NOx　prediction　model　is　instrumental　in　enhancing　the　precision　and　efficacy　of　the　prediction　model.　This　paper　focuses　on　the　input　variables　of　the　data-driven　diesel　engine　NOx　prediction　model.　According　to　the　diesel　engine　NOx　generation　mechanism,　at　first　relevant　variables　are　selected,　then　the　similarity　method　and　parameter　sensitivity　method　are　compared　to　optimize　the　input　variables.　Firstly,　this　paper　conducted　a　real-road　emission　test　of　a　heavy-duty　diesel　vehicle,　and　proposed　a　data-driven　deep　learning　model　for　predicting　diesel　engine　NOx　emissions　based　on　the　experimental　data.　The　experimental　data　were　processed　using　an　Attention-Mechanism　based　Convolutional　Neural　Networks-Long　Short-Term　Memory　(AM-CNN-LSTM)　model.　Subsequently,　input　parameters　were　selected　according　to　the　sensitivity　weights　assigned　to　each　parameter　in　the　preliminary　model.　This　approach　refined　the　modeling　data　and　excluded　variables　not　pertinent　to　NOx　emission　prediction.　The　AM-CNN-LSTM　model　prediction　performance　has　been　improved　with　2%　increase　of　model　R2　as　well　as　the　model　training　time　is　reduced　by　23%,　and　the　types　of　parameters　required　to　train　the　model　are　reduced　by　50%.　It　accomplished　this　by　effectively　reducing　data　dimensions,　discarding　non-essential　variables　for　NOx　prediction,　and　diminishing　computational　complexity.　Six　input　parameters　were　identified　as　pivotal　in　reconstructing　the　modeling　data　for　accurate　NOx　prediction:　engine　speed,　torque,　EGR　valve　position,　exhaust　flow　rate,　air　mass　flow　rate,　and　oil　temperature.　In　optimizing　the　NOx　prediction　model,　it　is　crucial　to　determine　an　optimal　number　of　input　parameters.　Excessive　parameters　do　not　enhance　model　accuracy,　while　an　insufficient　number　impairs　the　model＇s　ability　to　precisely　emulate　the　NOx　generation　process,　leading　to　diminished　predictive　accuracy.