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With the rapid development of rice phenotypic research, rice disease research has also made great progress as an important part of rice phenotypic research. In order to identify bacterial stripe disease quickly, accurately and effectively in the early stages of disease, a method for identifying bacterial stripe of rice based on a random forest algorithm was proposed. The spectral imaging technology was used to obtain hyperspectral data of the disease, and multiple noise correction was used to reduce and eliminate noise and the adverse effects of baseline drift on spectral data. Using the importance index of random forest characteristics, the logistic regression, naive Bayes, decision tree, support vector classifier, k-nearest neighbor and gradient boosting decision tree algorithms were selected for comparative test. At the same time, totally 12 spectral bands which were located in 450~664nm had an important influence on the recognition model were screened out. The results of classification based on the whole band and the 12 important bands were compared. The experimental results showed that the classification accuracy of RF algorithm was 95.24% compared with other algorithms selected in the experiment, the accuracy was higher than that of NB algorithm by 20.97 percentage points. Compared with the whole band classification results, based on these 12 important bands, the number of bands was reduced by 98.05%, the recognition accuracy was 94.66%, the recall rate was 99.55%, the F1 value was 97.04%, and the accuracy rate was 94.32%. Although the accuracy was reduced by 2.97 percentage points, the accuracy rate was reduced by 0.85 percentage points, the recall rate was increased by 4.4 percentage points, the F1 value was increased by 0.67 percentage points, and the model accuracy was basically maintained. Although the accuracy was reduced, the model structure was more streamlined and the computational complexity was reduced. The research result showed that important bands can be used instead of full bands to identify rice bacterial streak disease, which provided new ideas for the identification method of rice bacterial streak disease.