目的 研究一种更有效的光谱重建方法，以提升光谱重建的精度。方法 文中提出一种基于宽度学习的光谱重建方法，以包含1 269个色块的孟塞尔亚光数据集和包含289个色块的Agfa IT8.2数据集为实验样本，利用商用彩色数码相机的模拟系统对所提方法进行验证，以光谱均方根误差、光谱拟合优度系数和2种色差公式为算法评价指标，并与现有的光谱重建方法进行了对比。结果 实验结果表明，该方法可实现的平均均方根误差低至0.4%，平均光谱拟合优度系数达到99.9%，平均色差低至0.147和0.112，光谱精度和色度精度都明显优于其他2种方法。结论 基于宽度学习的光谱重建算法可以有效地提高光谱重建的精度，能够实现更高精度的光谱颜色表征和再现的要求。

The work aims to study a more effective spectral reconstruction method to improve the reconstruction precision. A spectral reconstruction method based on broad learning system was proposed. With Munsell matt color set of 1 269 color blocks and Agfa IT8.2 of 289 color blocks as experimental samples, the proposed method was verified by the simulation system of commercial color digital camera. The root mean square error of spectrum, goodness of fit coefficient of spectrum and two color difference formulas were used as the evaluation indexes to compare the proposed algorithm with the existing spectral reconstruction methods. The experimental results showed that the root mean square error of the method was 0.4%, the average spectral goodness of fit coefficient was 99.9%, and the average color difference was 0.147 and 0.112. The spectral precision and chromaticity precision were significantly better than those of other two methods. The spectral reconstruction algorithm based on broad learning system can effectively improve the precision of spectral reconstruction and meet the requirements of higher precision spectral color representation and reproduction.