Review　spam　detection　aims　to　detect　the　reviews　with　false　information　posted　by　the　spammers　on　social　media.　The　existing　methods　of　review　spam　detection　ignore　the　importance　of　the　information　hidden　in　the　user　interactive　behaviors　and　fail　to　extract　the　indistinct　contextual　features　caused　by　irregular　writing　style　of　reviews.　In　this　paper,　a　new　review　spam　detection　method　based　on　multi-dimensional　features　is　proposed.　The　method　utilizes　the　principal　component　analysis　to　get　low-dimensional　features　to　characterize　the　user-product　relationship.　Then,　a　neural　network　constructed　with　nested　LSTM　and　capsule　network　is　trained　to　extract　textual　context　features　and　spatial　structure　features.　Finally,　the　model　combines　the　text　and　user　behavioral　features　as　the　overall　features,　which　are　used　as　the　input　to　the　classification　module　to　detect　spam　reviews.　Experimental　results　show　that　the　F1　value　of　our　proposed　method　is　1.6%~3.5%　higher　than　the　existing　methods,　indicating　the　efficiency　and　effectiveness　of　our　model,　especially　on　the　natural　distribution　datasets.　©　2020,　Springer　Nature　Switzerland　AG.