The water absorption rate of flour is an important quality parameter for evaluating flour quality and predicting the processing characteristics of flour-based products. Determining the water absorption rate is mainly conducted using a gluten analyzer according to international or national standards, which is time-consuming and labor-intensive. Therefore, this study proposes using visible near-infrared spectroscopic analysis technology for rapid and non-destructive detection of the water absorption rate of flour. The water absorption rates of 150 wheat flour samples were determined according to the national standard method, and the value rang from 53.10% to 74.50%. The spectral information of the flour samples was collected using a visible near-infrared spectrometer, with an effective spectral range from 570 to 1 100 nm. Partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) was used to correlate the spectral information with the water absorption rate of flour. Quantitative analysis prediction models for the water absorption rate were established, and the optimal modeling methods were selected. Based on the selected modeling methods, competitive adaptive reweighted sampling (CARS), interval random frog leaping (iRF), iterative variable selection using the retained informative variables (IRIV), and successive projections algorithm (SPA) were employed to extract feature wavelengths and select the optimal feature wavelength extraction algorithm. Five spectral preprocessing methods, including normalization (NL), first derivative (1st Der), baseline correction (BL), standard normal variate (SNV), and detrending (DT), were applied to preprocess the spectral data of the feature wavelengths. The optimal spectral preprocessing method was determined. The results showed that the PLSR model built after preprocessing the spectra of the 24 feature wavelengths (only 2.26% of the original wavelengths) extracted by the CARS algorithm using the NL spectral preprocessing method achieved the best performance. The correlation coefficient (R2p), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD) for the prediction set were 0.889 4, 1.458 5, and 2.641 3, respectively. The model built using the feature wavelengths extracted by the CARS algorithm not only improved the models performance but also significantly increased the computational efficiency, reduced instrument manufacturing costs, and alleviated the challenges of miniaturizing the spectrometer. This study provides a foundation for the non-destructive and rapid detection of the water absorption rate of flour using visible near-infrared spectroscopy.