In this study the censored data of inductively coupled plasma mass spectrometry with support vector machine were employed in order to identify honeys according to their botanical source. 97 samples were collected for this study， including four kinds of honeys such as vitex honey samples， acacia honey samples， sunflower honey samples and rape honey samples. After pretreated by microwave digestion， the 16 kinds of metal elements in honey samples were measured by inductively coupled plasma mass spectrometry and 13 kinds of metal elements with significant differences were studied. The support vector machine classification model based on Gaussian radial basis function was established by using the metal elements with and without censored data as input variables. Then， the penalty parameter ,c, and the kernel function parameter ,g, of the support vector machine model were optimized by three optimization algorithms： grid search， genetic algorithm and particle swarm optimization. The result showed that there are 9 kinds of metal elements have censored data， namely Al， Ti， Cr， Ni， As， Se， Cd， Ba， Pb. The analysis of variance results showed that 12 kinds of metal elements such as Na， Mg， Al， K， Ca， Mn， Ni， Cu， Zn， Se， Ba and Pb have extremely significant differences in 95% confidence interval（,p,<, 0.01）， the element of As has significant differences in 95% confidence interval（,p,<, 0.05） and the elements of Ti， Cr and Cd have no significant differences in 95% confidence interval（,p,>, 0.05） among four different botanical source honeys. The censored data was processed to the one-half of the detection limit value by using the substitution method and the support vector machine model which established by censored data of metal elements as input variables has better results than the support vector machine model which without the censored data. The accuracy rate of the model established with censored data is 91.8%， while the accuracy rate of the model established without censored data is only 82.5%. Further optimization of penalty parameter ,c, and kernel function parameter ,g, in classification model by using grid search， genetic algorithm and particle swarm optimization， the support vector machine model with the penalty parameter ,c, of 62.8 and the kernel function parameter ,g, of 1.26 was the best by using particle swarm optimization. The correct rate of comprehensive discrimination of the best support vector machine classification model is 96.9%. It is concluded that it is feasible to identify honey botanical source through the substitution method which made the censored data to the one-half of the detection limit value and also shows that the optimized support vector machine model with censored data of inductively coupled plasma mass spectrometry can improve the accuracy of model discrimination and identify effectively honey samples from different botanical sources.