2025ApJS..280...24T

The Hα emission line commonly appears in the spectra of many stars and serves as a key indicator for tracing ionized interstellar gas, investigating stellar activity, and studying gas dynamics. Young stellar objects (YSOs), representing the early evolutionary stages of stars, typically exhibit the Hα emission line in their spectra. In this paper, we use bidirectional long short-term memory networks and convolutional neural networks to identify Hα emission-line stars in medium-resolution spectra from the Large Area Multi-Target Fiber Optic Spectroscopic Telescope (LAMOST) survey, and further search for YSO candidates via the Li absorption line. We constructed a data set by crossmatching previously published data sets with LAMOST data and performing manual verification. Using this data set, we built an identification model that achieved an accuracy of 97.58% on the testing set. Application of this model to the full survey yielded 46,867 Hα emission-line star candidates, with 41,996 visually confirmed detections (15,329 of which are recorded in SIMBAD). To further identify YSOs, we developed a dedicated Li absorption line detector, identifying 4618 preliminary candidates from the Hα emission-line stars. Rigorous vetting confirmed 4255 YSO candidates, comprising 3470 previously cataloged objects and 785 new discoveries. All catalogs (Hα emission-line stars and YSOs) and the code of the proposed model are publicly released to facilitate community research.