Multimodal　Knowledge　Graph　Completion　(MMKGC)　involves　integrating　information　from　various　modalities,　such　as　text　and　images,　into　traditional　knowledge　graphs　to　enhance　their　completeness　and　accuracy.　This　approach　leverages　the　complementary　nature　of　multimodal　data　to　strengthen　the　expressive　power　of　knowledge　graphs,　thereby　achieving　better　performance　in　tasks　like　knowledge　reasoning　and　information　retrieval.　However,　knowledge　graph　completion　models　designed　for　the　structural　information　of　triples　directly　applied　to　the　multimodal　domain　have　led　to　suboptimal　model　performance.　In　response　to　this　challenge,　this　study　introduces　a　novel　model　called　the　Multimodal　Knowledge　Graph　Completion　Model　Based　on　Modal　Hierarchical　Fusion　(MHF).　The　MHF　model　employs　a　phased　fusion　strategy　that　initially　learns　from　structured,　visual,　and　textual　modalities　independently.　Then,　it　combines　structural　embeddings　with　text　and　image　data　using　a　specially　designed　neural　network　fusion　layer　to　see　how　the　different　types　of　data　interact　with　each　other.　Additionally,　the　MHF　model　incorporates　a　semantic　constraint　layer　with　a　Factor　Interaction　Regularizer,　which　enhances　the　model’s　generalization　ability　by　exploiting　the　semantic　equivalence　between　the　head　and　tail　entities　of　triples.　Experimental　results　on　three　real-world　multimodal　benchmark　datasets　demonstrate　that　the　MHF　model　achieves　excellent　performance　in　link　prediction　tasks,　surpassing　the　current　state-of-the-art　baselines,　the　average　performance　gain　of　MRR,　Hit@1,　and　Hit@10　is　greater　than　5.4%.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.