Crop diseases can seriously restrict crop yield and quality. Traditional disease monitoring methods are inefficient and susceptible to subjective factors. Hyperspectral remote sensing technology has shown great potential in crop disease monitoring due to its high spectral resolution and objective authenticity. Ground hyperspectral and field disease index (DI) data of winter wheat with multiple growth stages were used, the spectral data were preprocessed using correlation analysis (CA) and successful projection algorithm (SPA) respectively, and the sensitive bands of wheat stripe rust through competitive adaptive reweighted sampling (CARS) algorithm that can construct optimal parameters were optimized. Finally, partial least squares regression (PLSR), back propagation neural network (BPNN) and extreme learning machine (ELM) were used to establish the disease index model based on the characteristic spectrum, and the modeling effects of different modeling methods were compared to realize the monitoring of wheat stripe rust. The research results indicated that the sensitive characteristic bands of wheat stripe rust in different growth stages were mainly concentrated in the near infrared and shortwave infrared bands, with 842nm, 850nm, and 858nm in the flag leaf stage and 947nm, 953nm, 1275nm, 1277nm, 1590nm, 1663nm, and 1665nm in the filling stage. In the comparison of different modeling methods, PLSR model performed best, and the model met the needs of early monitoring of wheat diseases and pests, and showed more obvious characteristics in the middle of the disease. During the flag leaf stage and filling stages, the PLSR models constructed with SPA-CARS-MCX and CA-CARS-MSC respectively had the best prediction performance. The R2 of the validation sets were 0.782 and 0.861, the RMSE were 0.022 and 0.094, and the RPD were 2.140 and 2.687, respectively. The algorithm constructed can provide ideas for monitoring wheat stripe rust at different growth stages.