This　paper　aims　to　explore　and　apply　supervised　prototype　contrastive　learning　and　instance　contrastive　learning　methods　to　address　the　task　of　herbal　medicine　classification.　Traditional　medicine　relies　heavily　on　herbal　medicine　as　a　vital　element　in　its　practice.　with　a　long　history　and　rich　experience　in　disease　treatment　and　prevention.　With　the　extensive　application　and　research　of　herbal　medicine,　its　classification　has　become　a　key　research　direction.　However,　traditional　herbal　medicine　classification　faces　challenges　of　sample　scarcity　and　class　imbalance,　which　limit　the　performance　and　accuracy　of　classification　algorithms.　To　overcome　these　problems,　we　propose　a　method　that　combines　supervised　prototype　contrastive　learning　and　instance　contrastive　learning.　Prototype　contrastive　learning(PCL)　is　a　method　that　improves　classification　performance　by　learning　compact　representations　of　classes,　while　Instance-wise　contrastive　learning(ICL)　enhances　the　robustness　and　generalization　ability　of　classifiers　by　contrasting　and　emphasizing　the　differences　between　entities　of　different　classes.　By　combining　these　two　methods,　we　aim　to　improve　the　accuracy　and　robustness　of　herbal　medicine　classification　tasks.　We　conducted　a　set　of　ablation　experiments　on　a　herbal　medicine　dataset.　The　experimental　results　demonstrate　significant　performance　improvements　in　the　herbal　medicine　classification　task　for　both　contrastive　learning　methods.　©　2023　IEEE.