The　representation　and　aggregation　of　attribute　information　is　the　key　to　address　the　multi-attribute　decision-making　(MADM)　problems.　Based　on　the　error　problem　of　attribute　information　given　by　experts　and　the　heterogeneous　relationship　between　attributes,　a　new　MADM　method　based　on　q-rung　orthopair　cloud　interaction　weighted　Maclaurin　symmetric　mean　(q-ROCIWMSM)　operator　is　proposed.　In　it,　this　method　firstly　considers　the　randomness　of　evaluation　information　given　by　experts,　integrates　the　q-rung　orthopair　fuzzy　(q-ROF)　set　and　cloud　model　and　define　the　new　concept　of　q-rung　orthopair　cloud　(q-ROC),　so　as　to　betray　the　error　distribution　characteristics　of　membership　and　non-membership　information　caused　by　randomness.　Then,　to　investigate　the　multi-layer　heterogeneous　relationship　among　membership　functions　and　different　quantitative　attributes,　the　interaction　operator　and　Maclaurin　symmetric　mean　(MSM)　operator　are　introduced　into　q-ROC　information,　and　the　q-ROCIWMSM　operator　is　proposed　to　aggregate　q-ROC　information　with　multi-dimensional　parameter　characteristics.　Thirdly,　a　framework　of　MADM　based　on　q-ROCIWMSM　operator　is　established.　Finally,　a　cross-border　e-commerce　consumption　decision-making　case　is　used　to　test　the　effectiveness　of　the　proposed　method.　At　the　same　time,　robustness　analysis　and　method　comparison　analysis　further　show　the　advantages　of　our　approach.　The　study　results　show　that　the　proposed　method　can　accurately　describe　the　error　distribution　characteristics　of　attribute　information　and　eliminate　the　adverse　effects　of　extreme　values　in　the　evaluation　information　on　the　decision-making　results.　In　addition,　the　method　proposed　in　this　paper　can　flexibly　reflect　the　multi-layer　heterogeneous　relationship　between　attributes,　and　has　strong　flexibility　and　applicability.