This study proposes an improved spectral reflectance reconstruction method to convert the response data captured by RGB digital cameras to the object surface spectral reflectance. Additionally, a system noise model is also proposed, which incorporates both signal-dependent and signal-independent components, thus rendering it more closely aligned with real-world conditions. Image data captured with RGB digital cameras under multiple LED light sources, and the inverse distance of the Euclidean distance between the training sample test samples in RGB color space is used as a weighting coefficient to reconstruct the spectral reflectance of the object surface, as accurately as possible. Experimental results show that the proposed method has better reconstruction accuracy than existing methods. The root mean square error values and the reconstructed goodness of fit coefficient higher than 0.9958 indicate that the spectral reconstruction performance has greatly improved compared to existing methods, thus proving the validity of the proposed weighting method.