Abstract: In order to weight the parameters in primary processing scientifically and objectively, the explanatory variables of moisture content in output tobacco from each processing step were screened by Pearson correlation matrix based on the steady state data samples of a whole processed batch of a selected specification of brand "Yunyan". The prediction models were established by random forest regression, and further verified by goodness of fit and NMSE(Normalized Mean Square Error)of test set of 5-fold cross-validation. Finally, the weights of explanatory variables were calculated and key parameters were screened by the average decrement of OOB (Out of bag) mean square error. The results showed that:1) Thirty-seven explanatory variables were screened out by Pearson correlation matrix combined with equipment control principle. 2)The goodness of fit of models for five processing steps was above 0.9 and NMSE of test set of 5-fold cross-validation was less than 1, which proved the fitting and extrapolation prediction performance of the models. 3)Eighteen key parameters were screened out on the basis of the weight of explanatory variables. 4)The proposed method for screening and weighting key parameters in primary processing provided a reference for the precise control of primary processing and the assessment of processing quality.