The contents of total phenol in wine grape are an important indicator of grape quality and also a key factor of wine quality directly. To detect the total phenol contents of the cabernet sauvignon grape quickly and accurately, this paper used near-infrared spectroscopy and GA-ELM prediction model to predict the total phenol content of Cabernet Sauvignon grapes. In the experiment, Cabernet Sauvignon grapes were collected in 5 harvest periods (40 bunches were collected in each harvest period, and 10 grapes were acquired in each cluster), and near-infrared spectra information in the range of 12 500~4 000 cm^-1 was collected for 200 groups of grapes. The total phenol content of Cabernet Sauvignon grapes was determined based on the principle of Folin-Ciocalteus colorimetry, SPXY algorithm was used to divide the samples into correction sets and prediction sets at a ratio of 3:1, with a total of 150 correction sets and 50 prediction sets. Multiplicative Scatter Correction (MSC),Standard Normalized Variate (SNV), Mean Centering (MC), Moving Average (MA), and the First Derivative +SG was used to preprocess the raw spectra, MSC was compared as the best pretreatment method.And then, competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), successive projections algorithm (SPA) and synergy interval partial least squares (si-PLS) were extracted the characteristic wavelengths, respectively. The comparative analysis found that the 69 characteristic wavelength variables extracted by CARS could effectively improve the model's stability and prediction ability. Based on the MSC and different variable optimization methods, the extreme learning machine (ELM) algorithm was introduced to establish the total phenol content prediction model. In predicting total phenol content, a genetic algorithm (GA) was used to optimize the ELM model and the influence of different kernel functions and the number of hidden layer neurons on the prediction ability of the GA-ELM model investigated. The optimal kernel function was Sigmoidal, and the optimal number of neurons was 50. Finally, the prediction capabilities of the ELM and GA-ELM models were compared. The results showed that GA-ELM models were more accurate in predicting than the ELM models, and the MSC+CARS+GA-ELM model was the best with a correlation coefficient of calibration (R_c) of 0.901 7, the correlation coefficient of prediction of 0.901 3, the root mean square error of calibration (RMSEC) of 2.112 4, the root mean square error of prediction (RMSEP) of 1.686 8 and residual prediction deviation (RPD) of 2.308 0. The combination of variable optimization methods and the GA-ELM model was an effective method, which provided a theoretical basis for detecting Cabernet Sauvignon grapes' quality.