Abstract:This study integrated machine learning methods and text mining into the shelf-life prediction research of marinated meat products.an original dataset was constructed based on the collection of shelf-life and influencing factors（packaging methods，storage methods，preservatives，and secondary sterilization）of marinated meat products in literature databases. By comparing various encoding methods（including JamesStein、BaseNEncoder、TargetEncoder、OrdinalEncoder、PolynomialEncoder），the James-Stein encoding was selected as the encoding method for categorical feature variables with better performance. Subsequently，through comparing various machine learning algorithms（including RandomForest，K-nearest neighbors，LogisticRegression，XGboost，and multi-layer perceptron classifier），the optimal model was found to be a random forest（with an accuracy of 0.95，precision of 0.97，AUC value of 0.99，and F1-score of 0.91）.Testing and analysis of actual samples of marinated beef and salted duck confirmed the high accuracy of the model in predicting the shelf-life of different marinated meat products. Moreover，this study validated the significant impact of factors such as storage temperature，packaging methods，preservatives，and secondary sterilization on the shelf-life of marinated meat products from another perspective.