Key technologies of artificial intelligence in electric power customer service

Abstract

As 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.