In order to improve the stability of the moisture content after cutting in the production process of silk making, the matching degree of the indexes between the moisture content at the outlet of loose moisture return, the moisture content at the outlet of moistening leaf feeding and the moisture content after cutting was guaranteed. The steady state data samples of “Yunyan (A)” brand silk production process was selected and the influence variables of the model were analyzed by Recursive Feature Elimination Method (RFE). Based on the temperature and humidity prediction model of the workshop, Monte Carlo simulation, Neural Network algorithm and XGBoost algorithm were used to establish the moisture content control model after cutting. The model was tested by comparing the predicted value with the actual value. Within the error range of ±0.15% of the process standard value, the accuracy of moisture content after cutting increased from 62.57% to 86.49%. CPK compliance rate increased from 91.44% to 97.30%. The prediction and control method of the moisture content after cutting based on machine learning can realize the coordination and accurate control of the process parameters before and after cutting, and effectively ensure the stability of the moisture content after cutting in the process of silk making.