This paper proposed an intent-extraction joint model by fusing various expertise, which integrated knowledge graphs of vertical domains so that it could identify hidden information by understanding the user’s original intention. The relationship be-tween intent detection and slot filling was explicitly modeled, and the two were jointly trained to improve the overall performance of the model. The proposed model was evaluated on public datasets, Airline Travel Information System (ATIS) and The Snips Voice Platform (SNIPS), and the results yielded were superior to those of other intent detection models. Additionally, experiments were conducted on the constructed industrial quality traceability questions (QTQ) and product repair dataset (PR), where the proposed model yielded the best results. Specifically, the accuracy of intent detection on the QTQ dataset was 87.2%, the F1 value of slot filling was 86.7%, and the entire-sentence accuracy was 75.6%. Meanwhile, the accuracy of intent detection on the PR dataset was 92.5%, the F1 value of slot filling was 90.1%, and the entire-sentence accuracy was 88.5%, which indicated improvements to varying degrees compared with other mainstream models. Experiments showed that the proposed model could perform intent detection tasks with high accuracy and generalizability in industrial fields.