Hyperparameter setteings.

To address the challenges of feature sparsity, semantic ambiguity, and insufficient feature extraction in sheep disease question classification, this paper proposes a novel model named Dual-Channel Feature Fusion Network for Sheep Diseases Question Classification (DFF-SDQC). The model leverages the CINO pre-trained model to generate dynamic word embeddings, thereby enriching semantic representations. Subsequently, global textual features are captured through BiLSTM, while deeper local contextual features are extracted using an attention mechanism. To further enhance the robustness and generalization of the model, a question-word attention mechanism is introduced, enabling the attention matrix to better capture the intentions expressed by interrogative words, thus strengthening the overall feature representation of the question. Finally, dual-channel feature information is fused to obtain the final textual representation. Experimental results on the D-SDQC and D-TQC datasets show that DFF-SDQC achieves an F1-score of 93.18% on D-SDQC, improving 2.22 percentage points over the strongest baseline, demonstrating the effectiveness of the dual-channel fusion and attention design.