Clouds　play　an　important　role　in　the　Earth＇s　climate　system;　however,　various　observational　methods　describe　clouds　differently,　leading　to　cloud　products　being　described　with　different　characteristics,　and　affecting　our　understanding　of　cloud　effects.　To　address　this　problem,　this　study　integrates　different　cloud　products　into　the　transfer-learning　procedure　of　a　deep-learning　model　and　determines　the　cloud　effective　radius　(CER),　cloud　optical　thickness　(COT),　and　cloud　top　height　(CTH)　from　Himawari-8　thermal　infrared　measurements.　The　retrieval　results　were　independently　evaluated　against　the　moderate-resolution　imaging　spectroradiometer　science　products　and　further　compared　with　Himawari-8　operational　products　during　the　day.　The　root　mean　squared　errors　(RMSEs)　of　the　model　for　the　CER,　COT,　and　CTH　were　4.490　mu　m　,　11.198,　and　1.904　km,　respectively,　which　are　lower　than　those　of　Himawari-8　operational　products　(RMSE:　11.172　mu　m　,　14.755,　and　2.860　km).　Moreover,　validation　results　against　active　sensors　show　that　the　model　performs　slightly　better　during　the　day　than　at　night,　and　both　are　generally　better　than　the　Himawari-8　operational　product.　Overall,　the　model　maintains　stable　performance　during　both　day　and　night,　and　its　accuracy　is　higher　than　that　of　Himawari-8　operational　products.