Abstract: For the purpose of intelligent identification of the steady state of a batch process in primary processing, a method based on change point detection theory was proposed. The primary processing data of a whole processed batch of a selected specification of cigarette brand "Yunyan" were researched via being divided dynamically into subintervals by mean-variance change point detection model and PELT algorithm, then the threshold of variance and/or the mean of the subintervals were set according to technology standard, the subintervals which conformed with the threshold conditions were extracted to form a steady state data set. Finally, the formed data set was compared with the data sets identified by two existing rules of steady state defining. The results showed that:1) Different steady state identification methods significantly affected the accuracy of batch process data measurement in primary processing. 2) The identification method based on change point detection theory featured better adaptability and identification effect. 3) Combining the said method with Shapiro-Wilk normality test via the built-in R language program in the information system, the online intelligent assessment of process capability was realizable. The proposed method provides a technical support for the intelligent identification of steady state of process in primary processing.