To　construction　effective　simulation　meta-models　for　complex　physical　simulation　system,　the　＇curse　of　dimension＇　and　the　＇uncertain　and　imprecise　information＇　problems　have　to　be　addressed　firstly.　Although　simulation　meta-models　based　on　neural　networks　can　obtain　well　performance,　the　fuzzy　inference　mechanism　of　domain　expert　for　practical　application　problems　cannot　be　simulated.　Thus,　some　prediction　results　may　be　contradicted　with　that　of　the　actual　physical　systems.　Aim　at　these　problems,　a　novelty　selective　ensemble　(SEN)　simulation　meta-modeling　approach　based　on　multiple　kernel　feature　extraction　and　fuzzy　inference　modeling　is　proposed　in　this　paper.　A　new　ensemble　construction　method　based　on　multiple　candidate　kernel　latent　features　is　used　to　produce　candidate　training　sub-samples.　Candidate　sub-models　based　on　Mamdani＇s　fuzzy　inference　are　constructed　with　these　training　sub-samples.　Brand　and　bound　(BB)　and　adaptive　weighting　fusion　(AWF)　algorithms　are　used　to　select　and　combine　the　ensemble　sub-models　to　obtain　final　SEN　simulation　meta-model.　Simulate　results　based　on　synthetic　data　show　that　the　proposed　approach　is　effective.　©　2017　IEEE.