Purpose　-　This　paper　addresses　the　limitations　of　current　graph　neural　network-based　recommendation　systems,　which　often　neglect　the　integration　of　side　information　and　the　modeling　of　complex　high-order　interactions　among　nodes.　The　research　motivation　stems　from　the　need　to　enhance　recommendation　performance　by　effectively　utilizing　all　available　data.　We　propose　a　novel　method　called　MSHCN,　which　leverages　hypergraph　neural　networks　to　integrate　side　information　and　model　complex　interactions,　thereby　improving　user　and　item　representations.　Design/methodology/approach　-　The　MSHCN　method　employs　a　hypergraph　structure　to　incorporate　various　types　of　side　information,　including　social　relationships　among　users　and　item　attributes,　which　are　essential　for　enriching　user　and　item　representations.　The　k-means　clustering　algorithm　is　utilized　to　create　item-associated　hypergraphs,　while　sentiment　analysis　on　user　reviews　refines　the　modeling　of　user　interests.　Additionally,　hypergraphs　are　constructed　for　user-user　and　item-item　interactions　based　on　interaction　similarity.　MSHCN　also　incorporates　contrastive　learning　as　an　auxiliary　task　to　enhance　the　representation　learning　process.　Findings　-　Extensive　experiments　demonstrate　that　MSHCN　significantly　outperforms　existing　recommendation　models,　particularly　in　its　ability　to　capture　and　utilize　side　information　and　high-order　interactions.　This　results　in　superior　user　and　item　representations　and　improved　recommendation　performance.　Originality/value　-　The　novelty　of　MSHCN　lies　in　its　use　of　a　hypergraph　structure　to　integrate　diverse　side　information　and　model　intricate　high-order　interactions.　The　incorporation　of　contrastive　learning　as　an　auxiliary　task　sets　it　apart　from　other　hypergraph-based　models,　providing　a　significant　enhancement　in　recommendation　accuracy.