In order to solve the problems of high dimension，data coupling with each other and nonlinearity in soft sensing of industrial processes，a new soft sensing method based on principal curves was proposed. The nonlinear regression model based on principal curves borrowed the basic idea of PLS and extracted latent variables by principal curves，and the correlation between dependent variables and independent variables was considered simultaneously.A polynomial function was used to fit nonlinear relations between latent variables in latent space.Data from nonlinear functions and VCM(vinyl chloride monomer)distillation columns respectively were used to validate the model.The results showed that the model could solve the nonlinearity problems which included high data coupling in the industrial process with less latent variables and more accurate predictions than traditional PLS model.