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A novel quantitative detection method for the fatty acid value of flour during storage was innovatively proposed based on the feature fusion of colorimetric sensor data and near-infrared spectra. A colorimetric sensor array was developed to collect colorimetric sensor data of flour samples in different storage periods. A portable near-infrared spectroscopy system was built to collect near-infrared spectra of flour samples in different storage periods. Principal component analysis was used to perform feature reduction on the preprocessed colorimetric sensor data and near-infrared spectra. In the back propagation neural network (BPNN) model calibration, the five-fold cross-validation method was used to optimize and determine the best principal components (PCs) of the single technique analysis model. The optimized PCs based on the single technique model were fused in the feature layer, and a BPNN analysis model based on the fusion feature was established to realize the rapid detection of the fatty acid value during the flour storage. Experimental results showed that the number of PCs in the best BPNN model based on the characteristics of the colorimetric sensor and the near-infrared spectra were three and four, respectively. The average values of correlation coefficient and root mean square error of prediction of the BPNN model based on the fusion features in the prediction set were 0.9276 and 1.9345mg/(100g), respectively. The overall results showed that compared with the single technique data analysis model, the detection accuracy and generalization performance of the colorimetric sensor data and the near-infrared spectral feature fusion model were improved. The results can provide a new technical method for high-precision in-situ monitoring of grain storage quality.