The　objective　of　image　captioning　is　to　empower　computers　to　generate　human-like　sentences　autonomously,　describing　a　provided　image.　To　tackle　the　challenges　of　insufficient　accuracy　in　image　feature　extraction　and　underutilization　of　visual　information,　we　present　a　Swin　Transformer-based　model　for　image　captioning　with　feature　enhancement　and　multi-stage　fusion　(Swin-Caption).　Initially,　the　Swin　Transformer　is　employed　in　the　capacity　of　an　encoder　for　extracting　images,　while　feature　enhancement　is　adopted　to　gather　additional　image　feature　information.　Subsequently,　a　multi-stage　image　and　semantic　fusion　module　is　constructed　to　utilize　the　semantic　information　from　past　time　steps.　Lastly,　a　two-layer　LSTM　is　utilized　to　decode　semantic　and　image　data,　generating　captions.　The　proposed　model　outperforms　the　baseline　model　in　experimental　tests　and　instance　analysis　on　the　public　datasets　Flickr8K,　Flickr30K,　and　MS-COCO.