The paper discusses the feasibility of identifying Fritillaria thunbergii treated by sulfur fumigation using laser-induced breakdown spectroscopy (LIBS) technology with the combination of chemometric methods. Spectral data of Fritillaria thunbergii samples of no sulfur fumigation (no SF), mild sulfur fumigation (mild SF) and severe sulfur fumigation (severe SF) were collected and preprocessed by wavelet transform and normalization. Discrimination models of support vector machine (SVM), extreme learning machine (ELM) and random forest (RF) were developed on full spectra (400.41～871.65 nm) and characteristic wavebands (400.41～600.02 nm), respectively. Results showed that three models developed on characteristic wavebands all obtained the same or even better performance than the corresponding models developed on full spectra, indicating the effectiveness of extracting characteristic wavebands. Among the models developed on characteristic wavebands, SVM model obtained the optimal performance, with calibration and prediction accuracy reaching 100% and 95.83% respectively. The overall results demonstrated that LIBS technology with a combination of characteristic wavebands extraction and chemometric methods could be used for identifying Fritillaria thunbergii treated by sulfur fumigation. This study provides an instruction for identifying traditional Chinese medicine and can help to establish a quality detecting and grading evaluating system for traditional Chinese medicine.