In order to explore the utilization value of agricultural waste，investigate the changes of physicochemical indexes related to maturity degree during composting，the original and mature compost samples were collected and analyzed. Meanwhile，in order to explore the optimal models for composting maturity prediction，the XGBoost，RandomForest，Support Vector Machine (SVM) and multiple nonlinear regression models were used to predict the compost maturity of training set samples. The prediction accuracy of four models was compared by using test set samples. Results showed that:the sheep，cow，pig，rabbit，and chicken manures original composts were alkalescence and the soluble salt concentrations were high in leach liquor. The contents of total nutrients (N，P，K) in chicken manure compost were the highest while the contents of copper，zinc，iron，manganese and organic matter in pig manure compost were the highest. The concentrations of copper，zinc，iron，manganese and germination index (GI) showed an obvious uptrend，while the pH，EC and organic matter contents showed a downtrend after maturity process. Organic matter content and water content of compost exerted the greatest influence on compost maturity，and the %IncMSE values were 14.92% and 13.61%，respectively. GI was predicted by constructing the XGBoost，RandomForest，and SVM machine learning model and the multiple regression nonlinear regression model. The water and organic matter content were selected to be feature variables for constructing models to accurately predicted the compost maturity. After the feature selection optimized，the R2 values for XGBoost，RandomForest，SVM and multiple regression nonlinear surface models were 0.994，0.871，0.908，0.800，respectively. The XGBoost exhibited the highest performance with the root-mean-square error and mean absolute error were 4.690% and 4.042%. Compared with unoptimized model，R2 of XGBoost，RandomForest and SVM increased by 41.39%，5.83% and 36.30%，respectively. The pH，water content，organic matter and GI changed significantly before and after maturity process，which were selected to be the evaluation index of compost maturity. Comprehensive analysis results showed that XGBoost model had the highest prediction accuracy for compost maturity，and feature selection was an effective method to improve the prediction accuracy of the model.