Multimodal　hazardous　chemical　risk　knowledge　graphs　are　gradually　becoming　a　critical　technical　foundation　for　industrial　safety　management,　offering　novel　pathways　for　intelligent　identification　and　risk　prediction　through　their　integration　capabilities　over　multi-source　heterogeneous　data.　However,　existing　multimodal　hazardous　chemical　knowledge　graph　face　significant　challenges　in　practical　construction,　including　uneven　distribution　across　modalities　and　severe　missingness　of　high-dimensional　feature　information.　These　issues　lead　to　incomplete　graph　structures,　negatively　impacting　the　accuracy　of　knowledge　reasoning　and　risk　prediction.　To　address　these　challenges,　this　paper　proposes　a　multimodal　knowledge　graph　completion　model,　named　HCMMKGC,　integrating　a　dual-channel　embedding　mechanism　and　generative　adversarial　optimization.　Specifically,　the　dual-channel　architecture　independently　models　high-　and　low-dimensional　multimodal　data,　preserving　complex　structural　details　while　improving　multimodal　semantic　consistency.　Additionally,　a　Generative　Adversarial　Network　is　introduced　to　synthesize　scarce　modality　samples,　alleviating　representation　bias　caused　by　modality　imbalance　and　thereby　enhancing　graph　completion　effectiveness　and　downstream　reasoning　performance.　The　experimental　findings　demonstrate　that　the　HCMMKGC　model　exhibits　strong　performance　on　the　HCKG-Text　and　HCKG-Visual　datasets,　with　an　MRR　of　0.453　and　0.414,　respectively.　The　model＇s　Hit@10　values　of　0.642　and　0.572　are　indicative　of　significant　improvement　over　the　existing　baseline　model.　These　results　underscore　the　model＇s　superior　generalization　capabilities　and　robustness.　©　2025　Elsevier　Ltd