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BP neural network has been widely used in building prediction models because of its good nonlinear fitting ability. However, the chemical process data not only has nonlinear characteristics, but also is difficult to avoid the influence of noise, which causes data fluctuation and affects the accuracy of prediction model. Therefore, a DAE-BP method for chemical process quality prediction based on denoising autoencoder fusion was proposed. Firstly, the unsupervised learning model denoising autoencoder is used to eliminate the noise of the initial data, which has the characteristics of noise robustness and can restore the original state of the data as far as possible in the case of data damage, which is conducive to further quality prediction. On this basis, the obtained data features are used as the input of BP neural network of supervised learning model to obtain reliable prediction results. The effectiveness of the method was verified by an example of chemical process of debutanizer column. The results were compared with the single BP algorithm, principal component analysis (PCA) and autoencoder (AE) improved BP algorithm. The results show that the prediction error of BP algorithm improved by DAE is 1.2%, which is 3.2% higher than that of the single BP algorithm, 2.3% higher than that of PCA-BP and 1.9% higher than that of AE-BP, showing the best prediction performance.