It　is　usually　necessary　to　obtain　task-specific　training　data　in　high-resource　source　languages　for　cross-lingual　text　classification.　However,　due　to　labeling　costs,　task　characteristics,　and　privacy　concerns,　collecting　such　data　is　often　unfeasible.　We　focus　on　how　to　improve　text　classification　in　low-resource　languages　in　the　absence　of　source　training　annotated　data.　To　effectively　transfer　resources,　we　propose　a　new　neural　network　framework(ATHG)　that　only　make　use　of　bilingual　lexicons　and　high-resource　languages＇s　task-independent　word　embeddings.　Firstly,　through　adversarial　training,　we　map　the　source　language　vocabulary　into　the　same　space　as　the　target　language　vocabulary,　optimizing　the　mapping　matrix.　Then,　considering　multiple　languages,　we　integrate　different　language　information　through　a　multi-step　aggregation　strategy.　Our　method　outperforms　pretrained　models　even　without　accessing　large　corpora.　©　2024　IEEE.