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The new generation of large sky area spectroscopic survey project has produced nearly 10 million low-resolution stellar spectra. Based on these spectroscopic data, this paper introduces a machine learning algorithm named The Cannon. This algorithm is completely based on the known spectroscopic data of stellar atmospheric parameters (effective temperature, surface gravity, and metal abundance, etc.), this algorithm builds the characteristic vector by means of data driving, and establishes the functional relation between spectral flux characteristics and stellar parameters. Then it is applied to the observed spectral data to calculate the atmospheric parameters. The main advantage of The Cannon is that it is not directly based on any stellar physical models, it has an even higher applicability. Moreover, because of the use of full-spectrum information, even for the spectra with a low signal-to-noise ratio (SNR), it still can obtain the parameter solutions of high reliability. This algorithm has significant advantages in the data processing and parameter determination of large-scale stellar spectra. In addition, this paper presents two examples of using The Cannon to obtain the stellar parameters of K and M giants from the LAMOST spectral data.

For the studies of intergalactic medium and galactic chemical evolution, it is a foundation to measure the abundances of metallic elements in the interstellar or intergalactic medium by using the absorption lines of damped Lyman alpha systems (DLAs) on the celestial spectra. Three methods exist for measuring the metal column density of gas clouds: the curve of growth (COG) method, the line profile fitting method (LPTM), and the apparent optical depth method (AODM). The COG method was developed in about half a century ago, so far it has been mainly applied in observations with a low spectral resolution. But the COG method is unaffected by the instrumental profile, and it is widely used in the case of optically-thin gas clouds. In recent years, most observations are done with high-resolution spectrographs, and in the case of the rapid development of computer technology, the LPTM and AODM, especially the LPTM, are more frequently adopted by most of astronomers. However, the LPTM has more free parameters to determine, so, it will consume more computing time, and sometimes will result in several convergent solutions. It will also introduce more deviations when one spectral line is blended seriously with other lines. The AODM is intermediate between the COG method and the LPTM, it shows favoritism to some people owing to its simplicity and reliability; but because of the possible existence of unresolved saturation structure in spectral lines, its application is restricted to some extent.