The　maturity　of　Industry　4.0　technologies　such　as　the　Internet　of　Things　and　cloud　computing　has　accelerated　the　development　of　various　platforms.　In　new　energy　vehicle　(NEV)　recommendation　platforms,　customer　reviews　have　been　well　recognized　for　their　ability　to　provide　value-added　information　to　customers　interested　in　purchasing　NEVs.　However,　the　countless　NEV　reviews　on　recommendation　platforms　make　it　difficult　for　consumers　to　select　their　preferred　NEV.　The　existing　NEV　recommendation　platforms　also　do　not　automatically　perform　fine-grained　sentiment　analysis　of　the　product　attributes　contained　in　reviews.　Consequently,　they　cannot　provide　personalized　purchase　recommendations　for　consumers.　To　this　end,　this　study　aims　to　propose　a　product　purchase　decision　support　method　based　on　sentiment　analysis　and　multi-attribute　decision-making　to　improve　the　accuracy　of　personalized　NEV　recommendation　platforms.　Sentiment　analysis　was　conducted　on　the　attribute　reviews　of　NEVs　on　a　product　recommendation　platform.　Subsequently,　the　positive,　negative,　and　neutral　sentiment　ratios　obtained　based　on　sentiment　analysis　were　regarded　as　q-rung　orthopair　fuzzy　numbers.　The　ratios　were　then　recognized　as　cumulative　prospect　theory　(CPT)　inputs.　The　prospect　values　of　each　NEV　under　each　attribute　were　calculated　and　further　aggregated　into　a　Muirhead　mean　operator　to　finally　obtain　the　product　rankings.　This　method　was　used　to　portray　the　consumers＇　decision-making　process　considering　various　situations　and　irrational　psychological　factors　(e.g.,　risk-preference　attitude).　The　results　show　that　our　proposal　can　recommend　NEVs　that　are　more　consistent　with　consumers＇　personalized　requirements.　To　conclude,　our　study　can　enhance　the　decision-making　support　capacity　of　product　recommendation　platforms　by　providing　sentiment　analysis　and　capturing　customers＇　preferences　for　product　attributes.　Additionally,　it　can　recommend　more　suitable　NEVs　to　meet　personalized　customer　requirements.