Important process or product quality parameters in chemical plants are difficult to measure with sensors for economic or technical reasons and soft measurement is an important solution to measure these key parameters. Aiming at the strong nonlinearity, low prediction accuracy, frequent dynamic changes, and severe collinear interference in actual chemical production processes, this article proposes a dynamic soft sensor model using novel nonlinear autoregression with external input (NARX) based on principal component analysis (PCA) and wavelet denoising (WD) (PCA-WD-NARX). The feature information of the sample data is extracted by the PCA and the collinearity is eliminated at the same time. Moreover, the noise in the data set is eliminated by WD to simplify the complexity of the learning data. Then, the NARX is used to construct the dynamic soft sensor model. Finally, the recommended model is applied to predict the acetic acid content of a purified terephthalic acid (PTA) plant. Through the evaluation indexes of the root-mean-square error (RMSE) and the coefficient of determination (R-2), the experimental results show that the proposed method has the most outstanding prediction accuracy and generalization ability among the NARX model, the NARX integrating the PCA (PCA-NARX) model, the NARX integrating the WD (WD-NARX) model, the Elman model, and the recursive radial basis function (RRBF).