Abstract: Atomic electronegativity interaction vector (AEIV) and atomic hybridization state index (AHSI) were used for establishing the quantitative structure-spectroscopy relationship (QSSR) model of 13C NMR chemical shifts of isodon diterpenoid compounds. Multiple linear regression (MLR) and computational neural network (CNN) were used to create the models, and the estimation stability and generalization ability of the models were strictly analyzed by both internal and external validations. The established MLR and CNN models were correlated with experimental values and the correlation coefficients of model estimation, leave-one-out (LOO) cross-validation (CV), and predicted values of external samples were R cum=0. 9724, R CV=0. 9723, Q ext=0. 9738(MLR); R cum=0. 9957, Q ext=0. 9956(CNN), respectively. The results indicated that CNN gave significantly better prediction of 13C NMR chemical shifts for isodon diterpenoids than MLR. Satisfactory results showed that AEIV and AHSI were obviously good for modeling 13C NMR chemical shifts of isodon diterpenoid compounds.