The initial efficiency is a very important criterion for carbon anode material of Li-ion battery. The relationship between initial efficiency and structure parameters of carbon anode material of Li-ion battery was investigated by an artificial intelligence approach called Random Forests using D 10 , D 50 , D 90 , BET specific surface area and TP density as inputs, initial efficiency as output. The results give good classification performance with 91% accuracy. The variable importance analysis results show the impact of 5 variables on the initial efficiency descends in the order of D 90 , TP density, BET specific surface area, D 50 and D 10 ; smaller D 90 and larger TP density have positive impact on initial efficiency. The contribution of BET specific surface area on classification is only 18.74%, which indicates the shortcoming of BET specific surface area as a widely used parameter for initial efficiency evaluation.