In the process industry, the change of working condition results in the decrease of the prediction accuracy of traditional softsensing models. Take the impact of industrial data continuity, sequence, multicollinearity, and huge amount of data on the model establishment into account, a multi-conditions soft sensor regression model framework based on the time-nearest neighbor Laplacian regularization is proposed. To solve the multicollinearity of industrial data, the proposed regression framework utilizes the nonlinear iterative partial least squares method. Meanwhile, the domain adaptation regular term is introduced to mitigate the influence of the change of working conditions on the model. On this basis, the time nearest neighbor Laplacian regular term is proposed, which can maintain the sequence structure of the data during the mapping process. And the model training time is greatly reduced to meet the industrial real-time requirement. In the experiment, the multi-conditions data set of the melamine polymerization process is taken as an example. The results show that compared with the traditional method of partial least squares regression, when the target conditions are conditions 1 to 4, the method in this paper reduces the average root mean square error by 30. 3% , 31. 4% , 29. 3% and 24. 1% , respectively. And compared using with the traditional function of total connecting, using the function of time-nearest neighbor connecting to construct the Laplacian regularization could deduce the training time of the four working conditions by 14. 11, 1. 01, 26. 43, 0. 71 s respectively, and indicated that the accuracy and the training time could be improved.