Abstract: This series of papers deal with ship--target recognition. The project is conducted by using fuzzy neural networks and basing on the spectra of ship radiated-noise. Based on the studies of a large amount of ship radiated--noise data, which has been collected from actual ships on the sea, we extracted effectively recognizable features. Such features include line-spectrum features, stationary and nonstationary spectrum features as well as rhythm features. Finally the categorization are tested by unknown samples on the sea, including, 33 surface ships, 8 underwater targets in 30 operating conditions. Methods for memorization and classification are also explored in the project. Paper (III) is the third in the series.It deals with the extraction method of modulation information in double-frequency power spectrum and the establishment of pattern plate of double-frequency spectrum and average power spectrum. To extract feature from double-frequency spectrum, the tendency of wave is subtracted from the wave of each chanllel and the modulation of high frequency is compensated. The modulation degree of lines is shown by relative value and converted to fuzzy value by fuzzy function. The pattern-plate of double-frequency spectrum memorizes stable line and its corresponding modulation strength. The pattern-plate of average power spectrum memorizes the spectra mean of typical samples and the standard variance.