Pre-eclampsia　(PE)　is　a　type　of　hypertensive　disorder　during　pregnancy,　which　is　a　serious　threat　to　the　life　of　mother　and　fetus.　It　is　a　placenta-derived　disease　that　results　in　placental　damage　and　necrosis　due　to　systemic　small　vessel　spasms　that　cause　pathological　changes　such　as　ischemia　and　hypoxia　and　oxidative　stress,　which　leads　to　fetal　and　maternal　damage.　In　this　study,　four　types　of　risk　factors,　namely,　clinical　epidemiology,　hemodynamics,　basic　biochemistry,　and　biomarkers,　were　used　for　the　initial　selection　of　model　parameters　related　to　PE,　and　factors　that　were　easily　available　and　clinically　recognized　as　being　associated　with　a　higher　risk　of　PE　were　selected　based　on　hospital　medical　record　data.　The　model　parameters　were　then　further　analyzed　and　screened　in　two　subgroups:　early-onset　pre-eclampsia　(EOPE)　and　late-onset　pre-eclampsia　(LOPE).　Dynamic　gestational　week　prediction　model　for　PE　using　decision　tree　ID3　algorithm　in　machine　learning.　Performance　of　the　model　was:　macro　average　(precision　=　76%,　recall　=　73%,　F1-score　=　75%),　weighted　average　(precision　=　88%,　recall　=　89%,　F1-score　=　89%)　and　overall　accuracy　is　86%.　In　this　study,　the　addition　of　the　dynamic　timeline　parameter　＂gestational　week　＂　made　the　model　more　convenient　for　clinical　application　and　achieved　effective　PE　subgroup　prediction.