Spectroscopic binaries and multiple-star systems are key samples for studying stellar formation, evolution, and dynamical interactions. However, identifying such systems in large spectroscopic surveys remains challenging because of their rarity, complex blended spectral features, and the high cost of visual inspection.
In this talk, I present a Human-AI hybrid approach for searching double-lined and triple-lined spectroscopic multiple systems in the LAMOST Medium-Resolution Spectroscopic Survey. The method combines the cross-correlation function (CCF), machine-learning classification, and human verification to improve the efficiency and reliability of candidate selection. I also compare several classifiers, including SVM, Transformer, DNN, CNN, and LSTM, under different training-data configurations, and evaluate their generalization ability using both observational and independent test sets.
Finally, I introduce how the candidate catalogs, CCF features, machine-learning results, and multi-source information from Gaia and existing binary/multiple-star catalogs are integrated into the China-VO multi-star system archive. The platform is designed not only as a data repository, but also as a tool for scientific discovery: it supports candidate cross-identification, visualization of spectra and classification results, identification of newly discovered systems, selection of special targets, and prioritization for follow-up observations. By combining large-scale survey mining with an online research platform, this work provides a practical pathway from spectroscopic candidate detection to scientific validation of multiple-star systems.