Abstract: Experimental determination of thermochemical conversion characteristics of multi-source or⁃ganic solid wastes is a time-consuming and labor-intensive process. By leveraging machine learningmethods, the correlation mechanism between different feedstock properties and thermochemical charac⁃teristics can be explored to enable fast and accurate prediction. A comprehensive dataset was construc⁃ted based on the fundamental properties and pyrolysis characteristics of 38 types of industrial organicsolid waste. Descriptive statistical analysis, correlation analysis, and principal component analysis(PCA) were employed to uncover patterns within the dataset. Subsequently, the random forest (RF),gradient boosting decision tree ( GBDT), and extreme gradient boosting ( XGBoost) algorithms wereutilized to predict the high heating value (HHV) of organic solid waste, the distribution of fast pyrolysisproducts, and the thermogravimetric curves under various atmospheres. The R values achieved forHHV, product distribution, and thermogravimetric curves ranged from 0.835 to 0.866, 0.701 to 0.875,and 0.976 to 0.980, respectively. Additionally, the Mean Decrease Impurity (MDI) and SHapley Ad⁃ditive exPlanations (SHAP) methods were applied to analyze the model′s performance and identify keyfeatures influencing the model′s decision-making process. This allowed for explaining the relationshipbetween feedstock properties and HHV. It also enabled explaining the connection between product dis⁃tribution and pyrolysis characteristics. This study aims to offer valuable insights into the intelligent man⁃agement and efficient disposal of organic solid waste.