Abstract: A novel soft sensor method based on slow feature reconstruction and enhancing dynamic partial least square (DPLS) is presented for process data with noise and dynamic characteristics.This method firstly extracts the components with slowly varying dynamics by a rising unsupervised learning method called slow feature analysis(SFA), which aims to extract invariant features and contain significant information from the high-dimensional signals. The extracted slow features are applied to reconstruct the raw input variables. In order to evaluate the reconstruction result, the reconstruction similarity index is proposed, which consists of the correlation between the original input and output and the similarity between reconstructed input and the original input, and the index realizes the purpose that original trend of data can be described by as few components as possible and the process noise is removed. Then, the reconstructed input variables are used for regression modeling. The traditional DPLS methods are built based on PLS and dynamic extension, which extend the input variables with time-delay inputs to describe the dynamic characteristics of process data. Although this method is easy to implement, the extended dynamic model may cause the curse of dimensionality and make the mapping matrix more complex and difficult to interpret when the dimension of input variables or time delay is too large. Therefore, the enhancing DPLS(EDPLS) is proposed by considering the importance of different time-delay input variables, and the complete flowchart of EDPLS, which consists of training and testing sections, is summarized, and the model built by EDPLS is more consistent with the dynamic relationship of process data than the general DPLS. Finally, soft sensing application of TE process and debutanizer column process have been carried out to test the effectiveness and feasibility of the proposed method, and the proposed method, SFAr-EDPLS, shows the better performance than the traditional dynamic regression models such as DPLS and EDPLS.