Flux normalization is a key procedure in spectral data mining, and is important for the efficiency and accuracy of automatic processing of massive astronomical spectral data, information extraction and sharing. Since the usual implementation of flux normalizing methods is inefficient, the present work focuses on the algorithm designing of spectral flux normalization. Firstly, the authors investigated the limit efficiency characteristics of the available flux normalization methods, introduced four efficient flux normalizing algorithms, and studied their time complexity and space complexity. Secondly, the authors evaluated the efficiency of the proposed algorithms experimentally and horizontally based on the SDSS (Sloan Digital Sky Survey) released spectral data. In the theoretical research, the main consideration is the computational complexity characteristics of the flux normalization methods when the data size increases unlimitedly. The experimental research focuses on the difference in the computational burden between the basic operations in different flux normalization methods. It is shown that, although the four flux normalization methods Smax, Smedian, Smean and Sunit belong to the same limit efficiency type, on the spectra with usual observing scale, Smax and Smedian are much more efficient than Smean and Sunit, and Sunit is the most inefficient one. This work is helpful for choosing the appropriate flux normalization method based on the size of spectra database and the scientific needs in automatic spectra analysis.