  The development of spectroscopic survey telescopes like Large Sky Area
Multi-Object Fiber Spectroscopic Telescope (LAMOST), Apache Point Observatory
Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up
unprecedented opportunities for stellar classification. Specific types of
stars, such as early-type emission-line stars and those with stellar winds, can
be distinguished by the profiles of their spectral lines. In this paper, we
introduce a method based on derivative spectroscopy (DS) designed to detect
signals within complex backgrounds and provide a preliminary estimation of
curve profiles. This method exhibits a unique advantage in identifying weak
signals and unusual spectral line profiles when compared to other popular line
detection methods. We validated our approach using synthesis spectra,
demonstrating that DS can detect emission signals three times fainter than
Gaussian fitting. Furthermore, we applied our method to 579,680 co-added
spectra from LAMOST Medium-Resolution Spectroscopic Survey, identifying 16,629
spectra with emission peaks around the H{\alpha} line from 10,963 stars. These
spectra were classified into three distinct morphological groups, resulting in
nine subclasses as follows. (1) Emission peak above the pseudo-continuum line
(single peak, double peaks, emission peak situated within an absorption line, P
Cygni profile, Inverse P Cygni profile); (2) Emission peak below the
pseudo-continuum line (sharp emission peak, double absorption peaks, emission
peak shifted to one side of the absorption line); (3) Emission peak between the
pseudo-continuum line.
