In　industrial　rubber　mixing　processes,　the　quality　index　(i.e.,　Mooney　viscosity)　cannot　be　online　measured　directly.　Traditional　data-driven　empirical　models　for　online　prediction　of　the　Mooney　viscosity　have　not　utilized　the　information　hidden　in　lots　of　unlabeled　data　(e.g.,　process　input　variables　during　each　mixing　batch).　A　simple　semi-supervised　nonlinear　soft　sensor　method　for　the　Mooney　viscosity　prediction　is　developed.　It　integrates　extreme　learning　machine　(ELM)　and　the　graph　Laplacian　regularization　into　a　unified　modeling　framework.　The　useful　information　in　unlabeled　data　can　be　explored　and　introduced　into　the　prediction　model.　Furthermore,　a　bagging-based　ensemble　strategy　is　combined　into　semi-supervised　ELM　(SELM)　to　obtain　more　accurate　predictions.　The　Mooney　viscosity　prediction　in　an　industrial　internal　mixer　exhibits　its　promising　prediction　performance　of　the　proposed　method　by　incorporating　the　information　in　unlabeled　data　efficiently.