In this work, we select the high signal-to-noise ratio spectra of stars fromthe LAMOST data andmap theirMK classes to the spectral features. Theequivalentwidths of the prominent spectral lines, playing the similar role asthe multi-color photometry, form a clean stellar locus well ordered by MKclasses. The advantage of the stellar locus in line indices is that it gives anatural and continuous classification of stars consistent with either thebroadly used MK classes or the stellar astrophysical parameters. We also employa SVM-based classification algorithm to assignMK classes to the LAMOST stellarspectra. We find that the completenesses of the classification are up to 90%for A and G type stars, while it is down to about 50% for OB and K type stars.About 40% of the OB and K type stars are mis-classified as A and G type stars,respectively. This is likely owe to the difference of the spectral featuresbetween the late B type and early A type stars or between the late G and earlyK type stars are very weak. The relative poor performance of the automatic MKclassification with SVM suggests that the directly use of the line indices toclassify stars is likely a more preferable choice.
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