the demand for customer service continues to increase

more companies are attempting to apply artificial intelligence technology in the field of customer service

enabling intelligent customer service

reducing customer service pressure

and reducing operating costs. Currently

the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions

and complex user expressions are difficult to understand. To solve the problem of low accuracy of multi-round dialogue semantic understanding

this paper proposes a semantic understanding model based on the fusion of a convolutional neural network (CNN) and attention. The model builds an “intention-slot” joint model based on the “encoding–decoding” framework and uses hidden semantic information that combines intent recognition and slot filling

avoiding the problem of information loss in traditional isolated tasks

and achieving end-to-end semantic understanding. Additionally

an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text

thereby increasing the accuracy of semantic understanding. Finally

by applying the model to electric power intelligent customer service

we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.