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This application discloses a method and system for predicting the outlet moisture content of tobacco shreds during the drying process based on a KAN network, relating to the field of tobacco technology. This method anchors key process parameters associated with the outlet moisture content of dried tobacco shreds, avoids interference from redundant variables, and adapts to the strongly coupled multi-variable operating conditions of tobacco drying. Specifically, step S1 completes the screening of key process variables to ensure a strong correlation between input features and the prediction target; step S2 enhances the robustness of the model under operating conditions through standardized processing adapted to the characteristics of industrial data; step S3 constructs a network structure that overcomes the nonlinear expression limitations of traditional fixed activation functions; step S4 optimizes model parameters to improve generalization performance and interpretability; step S5 verifies the model's generalization ability to ensure prediction stability under unseen operating conditions; and step S6 achieves real-time prediction of outlet moisture content, compensating for detection lag. Overall, it achieves high-precision and high-reliability prediction of the outlet moisture content of dried tobacco shreds, balancing prediction performance and industrial application value.