Several　difficult-to-measure　production　qualities　or　environment　pollution　indices　of　industrial　process　must　be　measured　using　offline　laboratory　instruments.　Soft　measurement　method　is　often　used　to　perform　online　prediction　of　such　parameters.　Only　small-sample　modeling　data　with　high-dimensional　input　features　can　be　obtained　due　to　the　limitations　and　complex　characteristics　of　the　measurement　device　and　process,　respectively.　Therefore,　a　new　multisource　latent　feature　selective　ensemble　(SEN)　modeling　approach　is　proposed　in　this　study.　First,　input　features　are　divided　into　different　subgroups　according　to　the　characteristics　of　the　modeling　data.　Second,　the　extracted　multisource　latent　features　evolve　from　the　multi-layered　selection　algorithms,　which　are　specified　by　feature　reduction　ratio,　feature　contribution　ratio　and　mutual　information　value　orderly　for　each　subgroup.　Finally,　in　order　to　construct　candidate　sub-models,　an　adaptive　hyper-parameter　selection　algorithm　based　on　the　multi-step　grid　search　is　employed　in　terms　of　the　reduced　features.　Sequentially,　the　optimized　ensemble　submodels　with　their　weighting　strategies　are　adaptively　determined　to　build　the　final　SEN　model.　The　proposed　method　is　verified　by　using　benchmark　near-infrared　data,　high　dimensional　mechanical　frequency　spectrum　data　and　industrial　dioxin　emission　concentration　data.