Image　emotion　classification　(IEC)　is　designed　to　predict　the　main　categories　of　emotional　tendencies　when　people　look　at　an　affective　image.　Unlike　common　image　classification　tasks,　IEC　suffers　from　conceptual　abstraction　and　high　annotation　costs.　The　language-supervised　methods,　i.e.,　SimEmotion,　are　designed　to　address　the　abstract　nature　of　emotion.　However,　it　is　still　trained　individually　on　a　specific　dataset　given　a　particular　classifier　and　cannot　effectively　use　valuable　annotated　data　from　other　source　domains,　which　limits　training.　We　propose　a　domain-aware　language-supervised　image　emotion　classification　prompt　learning　method,　DaLs.　Compared　to　current　language-supervised　methods,　DaLs　can　employ　fewer　parameters.　Moreover,　in　addition　to　aggregating　binary-category　datasets　with　the　same　category　labels,　our　approach　can　also　fuse　datasets　with　different　emotion　models　for　effective　multi-category　classification　experiments.　Evaluations　of　four　widely-used　affective　datasets,　demonstrate　that　the　proposed　algorithm　outperforms　the　state-of-the-art　methods　on　IEC　tasks.　©　2023　IEEE.