In order to realize accurate prediction of tea disease and avoid secondary damage in the process of disease feature extraction, the fluorescence transmission technology was used to study the spectrum characteristics of tea red leaf disease. The total of 45 samples of healthy tea leaves, 60 samples of early stage of red leaf disease and 60 samples of intermediate stage of red leaf disease were collected in the experiment, and were divided to training set and prediction set according to the proportion of 2∶1 for each kind. The original fluorescence transmission spectra of these leaves were collected using hyperspectral instrument by fluorescence transmission. Through the analysis of average spectral intensity curves of the three groups of leaves, the feasibility of using fluorescence transmission spectral information to classify the three types of leaves was confirmed. Then the polynomial smoothing (Savitzky-Golay, S-G) method was carried out for smoothing and noise reduction on the original spectral. Finally, competitive adaptive reweighted sampling (CARS) algorithm was used to select the characteristic wavelengths of the preprocessed spectral data. After 50 weighted samples, 4 characteristic wavelengths were selected finally, which were 463, 512, 586 and 613 nm respectively. In order to maximize the disease feature information of the samples and strengthen the typification of the classifier input value of disease feature, hyperspectral images were collected on 4 characteristic wavelengths respectively. Gray level co-occurrence matrix (GLCM) algorithm was used to extract image texture information, and 0°, 45°, 90°and 135° direction of the four gray level co-occurrence matrix were calculated. Then, the mean value and square error of the five symbiotic matrices were calculated, and the average value of the four image texture information was taken as the texture feature value of the leaf in order to enhance the recklessness. Finally, 10 feature values were obtained. The LBP (Local binary patterns) algorithm was used to extract the texture information from spectral image, and the uniform mode was used to reduce the dimension of LBP mode. Eventually, 944 dimension characteristic values of LBP were got from each image, similarly, the average value of the four images was taken as the characteristic value of LBP texture. Finally, the LBP eigenvalues of 944 dimensions were obtained for each image, and the average value of 4 images was also taken as the LBP texture feature value of the leaf. Finally, the prediction model was established under characteristic spectrum associated with the gray level co-occurrence matrix and the LBP operator respectively by using the extreme learning machine (ELM). As the input eigenvalues of the model were not in the same dimension, the input eigenvalues were normalized firstly, and then the output labels of the model were defined, that is, the output of the prediction model of healthy leaves was 1, the early stage of red leaf disease was 2, and the intermediate stage of red leaf disease was 3. The prediction accuracy based on CARS-GLCM-ELM model was 81.82%, and the prediction accuracy of CARS-LBP-ELM model was 85.45%. It showed that the effect of combining fluorescence transmission spectrum with LBP operator texture information was better. Due to the undesired results, the hidden layer activation function in ELM was optimized by using Softplus function instead of Sigmod function. The prediction accuracy of the optimized model was 92.73%. In this study, the fluorescence spectrum information of diseased leaves and texture information of hyperspectral images at corresponding characteristic wavelengths were fused, and the results can provide some reference for rapid and accurate prediction of tea diseases.