Knowledge　graph　completion　aims　to　expand　and　enhance　knowledge　graphs　by　predicting　missing　triples.　Multi-modal　knowledge　graph　completion　integrates　entity　ontology　information　such　as　entity　descriptions,　entity　images,　and　entity　attributes　to　obtain　more　accurate　entity　representations.　Existing　research　projects　different　modalities　into　a　unified　space　to　obtain　joint　representations　of　entities,　then　combine　knowledge　graph　structural　information　for　predictions.　However,　existing　methods　have　difficulty　in　capturing　the　complex　interactions　between　entity　background　knowledge　when　fusing　multi-modal　information,　which　inevitably　leads　to　information　loss　and　insufficient　feature　extraction　capabilities;　overfitting　and　limited　entity　relation　interactions　restrict　the　performance　of　2D　convolution　models,　making　it　difficult　to　integrate　knowledge　graph　structural　information.　Therefore,　this　paper　uses　a　multi-level　fusion　knowledge　graph　completion　model　to　address　the　above　issues　from　two　aspects:　the　fusion　of　entity　multimodal　information　and　the　integration　of　knowledge　graph　structural　information.　To　fully　integrate　entity　multi-modal　information,　three　different　fusion　methods　are　simultaneously　used　to　comprehensively　capture　the　interaction　of　entity　background　knowledge,　along　with　decision　learning,　aiming　to　combine　the　complementary　information　provided　by　different　multi-modal　fusion　methods　to　obtain　rich　and　diverse　entity　representations.　To　fully　integrate　knowledge　graph　structural　information,　feature　generalization　is　proposed　to　alleviate　the　overfitting　issues　of　2D　convolution　models,　combined　with　feature　reshaping　to　enhance　interactions　between　entities　and　relations,　thereby　improving　the　contextual　perception　ability　of　entities　and　relations.　Experiments　on　multiple　public　datasets　demonstrate　the　superior　performance　of　the　proposed　method.　©　2025　Journal　of　Computer　Engineering　and　Applications　Beijing　Co.,　Ltd.;　Science　Press.　All　rights　reserved.