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Rice is gradually aged during transportation and storage, and its degree of aging is an important factor affecting the quality of rice. A color-sensitive sensor array (CSA) combined with visible-near-infrared spectroscopy system was used to identify rice in different storage periods. Rice samples were stored under constant temperature and humidity conditions and divided into five groups according to different storage periods (0 month, 1 month, 2 months, 4 months and 6 months). CSA was made of three boron-dipyrromethene (BODIPY) dyes to capture the volatile organic compounds. After reacting with volatile organic compounds of rice in different storage periods, the spectral data of the color sensitive materials were separately extracted by the detection system. The optimal spectral range of the three types of spectral data was extracted using the Si-PLS algorithm. After the spectral interval data fusion, the characteristic spectral variables were extracted by genetic algorithm (GA), no information variable elimination method (UVE) and ant colony algorithm (ACO), respectively. Pattern recognition was performed by using principal component analysis (PCA) and linear discriminant analysis (LDA). The results showed that the LDA prediction model established by Si-PLS-UVE extracted spectral variables had the highest recognition rate. When the number of PCs was 9, the correct recognition rate of the calibration set and prediction set was 98% and 96%. The research result provided a viable method for detecting rice storage time.