In order to quickly and accurately detect the oil content of Camellia oleifera seed and solve the time-consuming and laborious problems of traditional detection methods, a method for detecting the oil content of Camellia oleifera seed based on hyperspectral imagery (HSI) was proposed. Two sets of hyperspectral imaging systems, spectral setⅠ (400~1000nm) and spectral setⅡ (900~1700nm), were used to collect diffuse reflectance hyperspectral images of Camellia oleifera seed, and the regression prediction model of oil content of Camellia oleifera seed was established in combination with chemometrics. The results showed that the partial least squares regression model (PLSR) established by the two sets of spectral data without pretreatment had the highest accuracy: the determination coefficient of prediction (R2p) of the spectral setⅠwas 0.681, and the root mean square error of prediction set (RMSEP) was 2.89%;R2p of spectral setⅡwas 0.740, and RMSEP was 2.92%. Comparing seven different variable selection methods, it was found that the two sets of spectral sets used genetic algorithm (GA) to filter the characteristic wavelength to establish the PLSR model with the highest accuracy: the spectral setⅠhad R2p of 0.694 and RMSEP of 2.82%;the spectral setⅡhad R2p of 0.779 and RMSEP of 2.54%. Comparing the modeling effects of spectral setⅠand spectral setⅡ, it was found that the performance of the PLSR model established by spectral setⅡwas better than that of the spectral setⅠ, so the band of 900~1700nm was more suitable for the oil content detection of Camellia oleifera seed than the band of 400~1000nm. Besides, the feasibility of using HSI to visualize the distribution of the predicted value of the oil content ofCamellia oleifera seed was further verified. This result can provide a method for the rapid detection of the oil content distribution of Camellia oleifera seed and the selection of high-quality its varieties.