Conversion velocity in the Polyvinylchlorid (PVC) polymerizing process has a certain influence on the molecular weight of PVC, porosity, absorption rate of plasticizer, vinyl chloride monomer (VCM) residue and thermal stability ii].Therefore a predictive model based on echo state networks (ESN) method optimized by the artificial fish swarm algorithm (AFSA) is proposed to predict the conversion velocity.A flowchart of the typical PVC polymerization kettle production process is shown in Figure 1.Firstly, the hot balancing mechanisms of polymerizer and the influenced factors of convention velocity of VCM are analyzed in details.Then the auxiliary variables of the predictive model kernel are selected by using the kernel principal component analysis (KPCA) method [2] for reducing the model dimensionality.