Unique organoleptic and flavor attributes of Jinhua ham are associated with their qualities. However, methods for quickly predicting the grade of hams, sensory scores and key flavor substances have not been systematically established. This study used sensory evaluation and E-nose to analyze the sensory differences for different grades of Jinhua ham. GC-MS was combined with O2PLS and correlation analysis to identify the key flavor substances. Classification model based on E-nose response signals was established by logical regression to predict the ham grades, which displayed a high classification performance, with the accuracy of 0.87. Moreover, linear regression and random forest models were established to predict the sensory score of hams and the concentrations of key flavor substances, meanwhile the R