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Rice blast is one of the three major rice diseases in the world, which poses a serious threat to food security of China. In order to reduce yield loss, it is urgent to establish a rapid and accurate method for monitoring and identifying rice leaf blast. Rice in northeast China is taken as the research object. Based on a plot experiment, hyperspectral images of rice leaves with different degrees of disease after infection by rice blast fungus were obtained through hyperspectral image analyzer, and spectral data was extracted. Firstly, the SG smoothing method was used to preprocess the spectral data, and then principal component analysis (PCA), Pearson correlation coefficient analysis (PCCs), and PLS-VIP method were used to reduce the dimensionality of the spectral data. An SVM classification detection model based on Logistic chaotic mapping PSO optimization (LMPSO-SVM) was proposed. To verify the effectiveness of the proposed method, classification models based on artificial neural network (ANN), support vector machine (SVM) and particle swarm optimization-support vector machine (PSO-SVM) were established by using feature variables extracted by different dimensionality reduction methods, and were compared and analyzed. The simulation results showed that each model had the best detection performance for level 4 samples. For these five levels of diseases, the prediction accuracy of SVM and ANN classification models fluctuated relatively large, and the effect of disease prediction was not ideal. The LMPSO-SVM classification model established under different feature selection had high accuracy for disease prediction at all levels, and the accuracy fluctuated less. The average accuracy of the model based on PCA extraction of feature variables and the whole band as input was very similar, with 96.49% and 96.12%, respectively. However, the number of input variables extracted by PCA was only 5, which greatly simplified the model complexity, reduced the difficulty and time of training. Comprehensive analysis showed that the PCA-LMPSO-SVM model had the best training effect and could be considered as the best disease classification model. The accuracy rates for the five levels of diseases were 94.29%, 96.43%, 93.44%, 9.30% and 100%, respectively. Therefore, the proposed method could further improve the accuracy and reliability of rice blast classification detection, and the results could provide a certain theoretical basis and technical support for the occurrence of rice blast diseases.