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ABSTRACT With the deep development of digital transformation in power grid enterprise trade unions, accurate identification of employee consultation intent has become a key technology for improving trade‐union service quality. To address the limitations of existing intent recognition methods in the trade union domain regarding domain adaptability, short‐text discrimination and statistically robust deployment validation, this paper presents a domain‐adaptive intent recognition solution that couples a purpose‐built trade‐union corpus with a Deep Residual Attention Fusion Network (DRAFNet). The corpus comprises 12,486 annotated consultations across seven intent categories, constructed through dual‐independent annotation plus expert adjudication with an overall Cohen's of 0.885 and a 30‐day‐later blind verification of 0.901 on a stratified random subset, and is processed under a leakage‐free split‐before‐augment data protocol. DRAFNet integrates domain‐lexicon‐enhanced encoding, a BERT backbone, four convolutional residual blocks with skip connections, and a gated fusion of local window attention and global multi‐head self‐attention, jointly optimized via cross‐entropy and focal loss. Over five independent runs on the self‐constructed test set, DRAFNet attains 94.52% 0.18% accuracy and 93.88% 0.21% Macro‐F1, statistically outperforming the strongest pretrained baseline ERNIE 3.0 by 2.21 and 2.25 percentage points respectively at < 0.001 in paired ‐tests. Cross‐corpus generalization is further verified on the public SMP2017‐ECDT Chinese intent benchmark, and a side‐by‐side comparison with three frontier large language models (GPT‐4o, Gemini 1.5 Pro, Claude 3.5 Sonnet) under zero‐shot and five‐shot prompting shows that DRAFNet outperforms the strongest LLM configuration by 6.36 percentage points in accuracy at over two orders of magnitude lower per‐sample latency. Ablation experiments verify the contribution of each module, and a distilled‐and‐quantized variant retains 92.15% Macro‐F1 while reducing storage footprint by 86% and CPU latency by 68%, demonstrating a favourable accuracy‐efficiency trade‐off for industrial deployment. The proposed integration provides a statistically robust and reproducibly evaluated solution for intent recognition in vertical service domains.
Song et al. (Sun,) studied this question.