The H{alpha} 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 H{alpha} emission line in their spectra. In this paper, we use bidirectional long short-term memory networks and convolutional neural networks to identify H{alpha} 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 H{alpha} 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 H{alpha} emission-line stars. Rigorous vetting confirmed 4255 YSO candidates, comprising 3470 previously cataloged objects and 785 new discoveries. All catalogs (H{alpha} emission-line stars and YSOs) and the code of the proposed model are publicly released to facilitate community research.