The　dynamic　response　of　the　seat-occupant　system　to　vibration　exhibits　complex　dependencies　on　various　factors,　including　anthropometric　parameters,　the　seat　structure,　and　riding　environments,　etc.　Drawing　from　an　experimental　investigation　into　whole　body　vibration,　this　study　aims　to　develop　a　predictive　model　for　seat　transmissibility　using　support　vector　machine　regression.　The　recursive　feature　elimination　method　and　principal　component　analysis　are　employed　to　reduce　the　dimensionality　of　anthropometric　parameters.　The　resultant　low-dimensional　features　are　then　integrated　into　the　model　to　predict　the　seat　transmissibility　and　cross-axis　effect.　The　results　show　that　compared　with　the　traditional　support　vector　machine　regression　model,　the　application　of　principal　component　analysis　to　reduce　the　correlation　of　anthropometric　parameters　can　significantly　reduce　the　prediction　error　of　the　model,　and　the　fitting　degree　between　the　predicted　value　and　the　measured　value　can　reach　92%.　The　accuracy　of　prediction　can　be　further　improved　by　removing　the　minor　anthropometric　parameters　through　the　recursive　feature　elimination　method,　and　the　fitting　degree　between　the　predicted　value　and　the　measured　value　is　94%.　The　findings　demonstrate　that　the　support　vector　machine　regression　model,　based　on　the　feature　dimensionality　reduction　optimization,　can　effectively　sieves　out　the　redundant　information　of　input　parameters　in　the　model.　This　refinement　enhances　both　the　efficiency　and　accuracy　of　predicting　the　seat　transmissibility.　©　2024　Science　Press.　All　rights　reserved.