In this study, we propose a two-stage deep learning model called the hot subdwarf network (HsdNet), which integrates multiple advanced techniques, comprising a binary classification model in stage one and a five-class classification model in stage two. We applied HsdNet to the 601,217 spectra from the LAMOST DR12-V1 database, conducting a two-stage search for hot subdwarf candidates. Finally, we confirmed 1008 newly identified hot subdwarf stars.  This catalog provides  LAMOST_obsid, LAMOST_uid, LAMOST_R.A., LAMOST_Decl., LAMOST_S/N-g, LAMOST_S/N-r, Teff, logg, and log(nHe/nH).