The multispectral dimension reduction method based on principal component and weighted principal component realizes the mutual conversion between multispectral data and low dimensional spatial data. However, the low dimensional spatial data contains a large number of negative values and cannot be connected with chromaticity space such as CIELAB, which brings difficulties to the follow-up research of spectral color replication; Establish the conversion from XYZ tristimulus to multispectral data, and retain more color information in the process of reducing the dimension of multispectral data to XYZ tristimulus value; The corresponding relationship between tristimulus values and three-dimensional space obtained by dimensionality reduction through weighted principal components is established through second-order polynomial regression, and the mutual conversion relationship between multispectral data and tristimulus values is realized; In different training samples and different test samples, compared with the principal component and weighted principal component, the proposed method improves the accuracy of colorimetric reconstruction under various lighting conditions, and can be better applied to the high fidelity dimensionality reduction and compression of multispectral images.