〔Abstract〕 Objective To rapidly identify triazole(fluconazole, voliconazole, iriconazole) drug resistance and sensitiveCandida tropicalusing matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry(MALDI-TOF MS) platform data analysis and machine learning algorithms. Methods A total of 191Candida tropicalwere collected from various clinical specimens, 71 of which were triazole-resistantCandida tropicaland 120 were triazole-sensitiveCandida tropicalstrains. Data acquisition was performed using the MALDI-TOF MS platform, and the mass and charge ratio features of resistant and susceptible strains were classified and selected based on the Mann-Whitney Rank-sum Test(Mann-WhitneyU-test) and the importance score obtained by the Random Forest(RF) algorithm. The classification model was constructed using the RF algorithm and a nonlinear support vector machine with a radial basis function kernel(RBF-SVM), calculating the accuracy, sensitivity, specificity, F1 value and the area under the subject worker curve(AUC) of the RBF-SVM model under the same experimental data to evaluate the model discrimination performance. Results All strains obtained 76 unique mass spectrum peaks after analysis on the MALDI-TOF MS platform. Among them, six peaks 3 481,7 549,6 500,3 048,6 892,2 596 m/z were selected as the model feature peaks established by the feature dimensionality reduction treatment. The accuracy of both the RBF-SVM and RF models was 0.84, and the AUC scores were 0.930 5 and 0.927 3, respectively. Conclusion Machine learning algorithms combined with the MALDI-TOF MS platform for data analysis can serve as a possible method to rapidly distinguish triazole-resistantCandida tropicaland triazole-sensitive strains.