Anti-inflammatory　peptides　(AIEs)　have　recently　emerged　as　promising　therapeutic　agent　for　treatment　of　various　inflammatory　diseases,　such　as　rheumatoid　arthritis　and　Alzheimer＇s　disease.　Therefore,　detecting　the　correlation　between　amino　acid　sequence　and　its　anti-inflammatory　property　is　of　great　importance　for　the　discovery　of　new　AIEs.　To　address　this　issue,　we　propose　a　novel　prediction　tool　for　accurate　identification　of　peptides　as　anti-inflammatory　epitopes　or　non　anti-inflammatory　epitopes.　Most　of　all,　we　encode　the　original　peptide　sequence　for　better　mining　and　exploring　the　information　and　patterns,　based　on　the　three　feature　representations　as　amino　acid　contact,　position　specific　scoring　matrix,　physicochemical　property.　At　the　same　time,　we　exploit　several　feature　extraction　models　and　utilize　one　feature　selection　model,　in　order　to　construct　many　base　classifiers　from　various　feature　representations.　More　specifically,　we　develop　an　effective　classification　model,　with　which　we　can　extract　and　learn　a　set　of　informative　features　from　the　ensemble　classifier　chain　model　with　different　group　of　base　classifiers.　Furthermore,　in　order　to　test　the　predictive　power　of　our　model,　we　conduct　the　comparative　experiments　on　the　leave-one-out　cross-validation　and　the　independent　test.　It　shows　that　our　novel　predictor　performs　great　accurate　for　identification　of　AIEs　as　well　as　existing　outstanding　prediction　tools.　Source　codes　are　available　at　https://github.com/guofei-tju/Ensemble-classifier-chain-model.