Modeling high‐dimensional dynamic processes is a challenging task. In this regard, probabilistic slow feature analysis (PSFA) is revealed to be advantageous for dynamic soft sensor modeling, which can extract slowly varying intrinsic features from high‐dimensional data. However, nonlinearities prevalent in industrial processes are not considered, which could lead to unsatisfactory prediction performance. In this paper, a weighted PSFA (WPSFA)‐based soft sensor model is proposed for nonlinear dynamic chemical process. In WPSFA, a weighted log‐likelihood function of complete data is constructed to linearize the nonlinear state emission equation. Then, the expectation maximization algorithm is applied to estimate the model parameters and a locally weighted regression model is established for quality prediction. Finally, the feasibility and effectiveness of the proposed approach are well illustrated through a numerical example and a real industrial process. A data‐driven soft sensor based on weighted probabilistic slow feature analysis is proposed for nonlinear dynamic processes. A nonlinear model structure is designed, in which a weighted log‐likelihood function of complete data is constructed to linearize the nonlinear state emission equation. The feasibility and effectiveness of the proposed approach are illustrated through a numerical example and a real industrial process.