In　the　process　of　online　shopping,　consumers　usually　compare　the　review　information　of　the　same　product　in　different　e-commerce　platforms.　The　sentiment　orientation　of　online　reviews　from　different　platforms　interactively　influences　on　consumers＇　purchase　decision.　However,　due　to　the　limitation　of　the　ability　to　process　information　manually,　it　is　difficult　for　a　consumer　to　accurately　identify　the　sentiment　orientation　of　all　reviews　one　by　one　and　describe　the　process　of　their　interactive　influence.　To　this　end,　we　proposed　an　online　shopping　support　model　using　deep-learning-based　opinion　mining　and　q-rung　orthopair　fuzzy　interaction　weighted　Heronian　mean　(q-ROFIWHM)　operators.　First,　in　the　proposed　method,　the　deep-learning　model　is　used　to　automatically　extract　different　product　attribute　words　and　opinion　words　from　online　reviews,　and　match　the　corresponding　attribute-opinion　pairs;　meanwhile,　the　sentiment　dictionary　is　used　to　calculate　sentiment　orientation,　including　positive,　negative,　and　neutral　sentiments.　Second,　the　proportions　of　the　three　kinds　of　sentiments　about　each　attribute　of　the　same　product　are　calculated.　According　to　the　proportion　value　of　attribute　sentiment　from　different　platforms,　the　sentiment　information　is　converted　into　multiple　cross-decision　matrices,　which　are　represented　by　the　q-rung　orthopair　fuzzy　set.　Third,　considering　the　interactive　characteristics　of　decision　matrix,　the　q-ROFIWHM　operators　are　proposed　to　aggregate　this　cross-decision　information,　and　then　the　ranking　result　was　determined　by　score　function　to　support　consumers＇　purchase　decisions.　Finally,　an　actual　example　of　mobile　phone　purchase　is　given　to　verify　the　rationality　of　the　proposed　method,　and　the　sensitivity　and　the　comparison　analysis　are　used　to　show　its　effectiveness　and　superiority.