The distributed and parallel computation was introduced to spectroscopy signal processing. The reflection spectra of 4 different varieties of sugar including sucrose, xylitol, maltose and dextrose were measured with FI/IR-4100 Fourier infrared spectral equipment. Each type of sugar consisted of 39 samples. The distributed and parallel algorithm was executed on 2 computers with the same hardware and software systems. First, the distributed and parallel algorithm was used to read original spectral data from the text files generated by FT/IR-4100 device. Second, the data were preprocessed by distributed and parallel algorithm. The preprocessing methods include standard normalization to the maximum peak, Savizky-Golay smoothing denoising, etc. Third, search for the key discriminative wave numbers in mass spectrometry data was performed by distributed and parallel genetic algorithm (GA). At the end, the discriminative features of 24 wave numbers extracted by GA were applied as BP neural network inputs and a 3-layer neural network was built up. The computing results generated by distributed and parallel algorithm are the same as the serial computing results generated by single personal computer. The processing efficiency using 2 personal computers is 33.6% higher than that of serial computation. So the paper presents a creative method for the complex scientific computation and enhancing the computing efficiency in spectroscopy signal processing.