The　rapid　and　accurate　prediction　of　self-diffusion　coefficients　for　pure　fluids　and　mutual　diffusion　coefficients　for　binary　mixtures　remains　a　focal　point　of　current　research.　In　this　work,　molecular　descriptors　are　innovatively　defined　by　introducing　molecular　repulsive　and　attractive　pressures　based　on　the　Carnahan-Starling　equation　of　state.　This　approach　aims　to　improve　the　quantitative　structure–property　relationship　(QSPR)　models　for　predicting　thermophysical　properties　that　vary　with　thermodynamic　conditions.　The　shapely　additive　explanation　method　is　employed　for　a　comprehensive　analysis　of　the　model　mechanism.　Among　the　six　molecular　descriptors　selected,　our　defined　attractive　pressure　(patt),　MATS2e　and　SpMin3_Bh(v)　contribute　significantly　to　the　self-diffusion　coefficient　QSPR　model.　Based　on　this　approach,　QSPR　models　are　constructed　for　15　hydrocarbons　and　five　of　their　binary　mixtures.　The　results　demonstrate　that　the　GA-BPNN-based　QSPR　model　for　pure　fluids　achieves　high　prediction　accuracy,　with　an　external　Rext2　of　0.978　and　an　external　RMSEext　of　5.349　×　10−9·m2·s−1.　The　optimal　mixture　descriptor　type　is　x1d1+x2d2,　and　the　GS-SVM-based　mixture　QSPR　model　predicts　98.24　%　of　values　within　a　5　%　error　margin,　with　Rext2　of　0.991　and　RMSEext　of　0.042　×　10−9·m2·s−1.　©　2024　Elsevier　B.V.