To address the lack of annotated data and insufficient performance in intent detection and slot filling in the domain of industrial equipment failure, a joint model based on BERT was proposed. BERT was utilized for text sequence encoding, while a bidirectional long short-term memory (Bi-LSTM) network was employed to capture the semantic relationships within the context. Max pooling and dense layers were used to extract key information, and a conditional random field (CRF) was incorporated to enhance the model's generalization capability. A question-and-answer corpus specifically tailored to the industrial domain of equipment failure was constructed, and a deployment framework for this domain was proposed. Experimental evaluations conducted on public datasets such as ATIS demonstrated that the proposed model outperforms baseline models by improving sentence-level accuracy, F₁ score, and intent detection accuracy by 4.4%, 2.1%, and 0.5% respectively. This research effectively enhances the performance of question-and-answer systems and provides a dataset and deployment framework for the field of industrial equipment failure, which lacks sufficient real-world data.