In order to reduce the number of defective products caused by the unreasonable baking time during the tobacco production process, this paper proposes a method for establishing a multi-model reasoning tobacco baking quality prediction model. Conduct data mining and analysis on the data of various indicators of the original tobacco, and screen out the data that have an impact on the quality of tobacco baking. In order to reduce the complexity of the model and eliminate the influence between different dimensions, the data are carried out and standardized processing. Next, the normalized data is explored for the multi-input and multi-output mapping relationship. Finally, a mapping matrix is given for the multi-input and multi-output mapping relationship so as to establish a tobacco baking quality prediction model. The test results show that the predicted value of this model is basically the actual value, and the prediction accuracy rate is more than 90%. It has a high prediction accuracy rate. The cured tobacco leaves are basically the same as the actual cured yellow expected value. This model provides a practical guide method for tobacco baking, which has certain practical value in actual tobacco baking.