A novel chemical soft-sensor approach for the prediction of the melt index (MI) in the propylene polymerization industry is presented. The MI is considered as one of the important variables of quality that determine the product specifications. Thus, a reliable estimation of the MI is crucial in quality control. An accurate optimal predictive model of MI values with the relevance vector machine (RVM) is proposed, where the RVM is employed to build the MI prediction model; a modified particle swarm optimization (MPSO) algorithm is then introduced to optimize the parameter of the RVM, and the MPSO-RVM model is thereby developed. An online correcting strategy (OCS) is further carried out to update the modeling data and to revise the model's parameter self-adaptively whenever model mismatch happens. Based on the data from a real polypropylene production plant, a detailed comparison is carried out among the least squares support vector machine (LS-SVM), RVM, MPSO-RVM, and OCS-MPSO-RVM models. The research results reveal the prediction accuracy and validity of the proposed approach.