The　transportation　industry　has　seen　a　rapid　increase　in　ships　used　for　inland　waterway　transportation.　Ship　classification　is　required　to　avoid　inland　waterway　collisions.　The　machine　learning　model　uses　an　LightGBM　classifier　for　classification.　The　transfer　learning　(TL)　model　uses　Inception-ResNet-v2,　DenseNet121　and　EfficientNetB0.　A　novel　dynamic　weighted　ensemble　classifier　based　on　the　game　theory　approach　is　proposed　for　the　fusion　of　the　outputs　of　the　TL　model　and　the　LightGBM　classifier　model.　Comparing　the　ensemble　approach　against　individual　deep　learning　(DL)　and　feature-based　models,　it　produces　excellent　performance　and　significantly　increases　the　accuracy　of　ship　classification.