Influenza A Virus (IAV) poses a continuous threat to global public health due to its wide host adaptability, high-frequency antigenic variation, and potential for cross-species transmission. Accurate recognition of IAV subtypes is cru-cial for the early pandemic warning. Here, we propose Influ-BERT, a domain-adaptive pretraining model based on the transformer architecture. Optimized from DNABERT-2, Influ-BERT constructed a dedicated corpus of approximately 900,000 influenza genome sequences, developed a custom Byte Pair Encoding (BPE) tokenizer, and employ a two-stage training strategy involving domain-adaptive pretraining followed by task-specific fine-tuning. This approach significantly enhanced recognition performance for low-frequency subtypes. Experimental results demonstrate that Influ-BERT outper-forms traditional machine learning methods and general genomic language models (DNABERT-2, MegaDNA) in subtype recognition, achieving a substantial improvement in F1-score, particularly for subtypes H5N8, H5N1, H7N9, H9N2. Furthermore, sliding window perturbation analysis revealed the model´s specific focus on key regions of the IAV genome, providing interpretable evidence supporting the observed performance gains.