Abstract: Moisture is the medium of various reactions inside the leaves during the processing of green tea, and it is an important factor affecting the quality of green tea. This studymeasured and recorded the moisture changes in each process of the tea processing process,and then studied and analyzed its dynamic changes. The results showed that the water loss process of fresh tea leaves was mainly concentrated in the process of fixation and drying. The process of spreading green water loss was less and the process was soft. The moisture was redistributed during the regaining process, and the tea was shaped by the rolling process, after/during whichthe moisture change was not obvious. Select the main dehydration processes in the two processing processes of fixation and drying, fix the drum speed, take temperature, initial moisture content and working time as input, and time moisture content as output, using BP neural network algorithm and support vector machine（SVM） algorithm to establish a moisture content prediction model for green tea fixation and drying. The established model was used for prediction, and the actual measured moisture content was compared and analyzed. The results showed that the R2 of the BP and SVM prediction models for the fixation were 0.99901 and 0.99932, respectively, while the R2 of the drying process were 0.99729 and 0.99786. The prediction model built by the SVM algorithm performed better and the prediction was more accurate. Compared with the drying process, the model had higher accuracy and better effect in the prediction of the moisture content of the fixation with higher moisture content and more obvious changes.