The Large Sky Area Multi-Object Fibre Spectroscopic Telescope data release 5 (DR5) released more than 200,000 low-resolution spectra of early-type stars with a signal-to-noise ratio >50. The search for metallic-line (Am) stars in such a large database and a study of their statistical properties are presented in this paper. Six machine-learning algorithms were experimented with using known Am spectra, and both the empirical criteria method and the MKCLASS package were also investigated. Comparing their performance, the random forest (RF) algorithm won, not only because the RF has high successful rate, but because it can also derive rank features. Then the RF was applied to the early-type stars of DR5, and 15269 Am candidates were picked out. Manual identification was conducted based on the spectral features derived from the RF algorithm; 9372 Am stars and 1131 Ap candidates were compiled into a catalog. Statistical studies were conducted including temperature distribution, space distribution, and infrared photometry. The spectral types of Am stars are mainly between F0 and A4 with a peak around A7, which is similar to previous works. With the Gaia distances, we calculated the vertical height Z from the Galactic plane for each Am star. The distribution of Z suggests that the incidence rate of Am stars shows a descending gradient with an increasing |Z|. On the other hand, Am stars do not show a noteworthy pattern in the infrared band. As the wavelength gets longer, the infrared excess of Am stars decreases, until there is little or no excess in W1 and W2 bands.