Aiming at the problem of the actual process industry process with dynamic time-varying and concept drift characteristics, which leads to a decrease in the prediction accuracy of the soft sensor model, a dynamic regression migration model based on low-rank reconstruction representation is proposed. In order to better describe the dynamic process, under the dynamic internal model partial least squares framework, the high-dimensional process data is mapped to the low-dimensional latent variable space to capture the dynamic correlation between quality data and latent variables. In order to reduce concept drift, while obtaining dynamic correlation, the conditional distribution alignment of data is achieved by enhancing the correlation between the estimated values of quality variables in different working conditions. Compared with the static base model and the dynamic base model, the experimental results on the three public industrial datasets improved, indicating that the proposed method can effectively improve the prediction accuracy and generalization ability of the model.