随着客户服务需求的不断增加,越来越多的企业尝试将人工智能技术应用在客服领域,通过智能客服实现客户服务赋能,减轻客服压力,减少运营成本.当前,现有的智能客服智能化程度有限,只能回答简单的用户问题,难以理解复杂的用户表述.为了解决多轮对话语义理解准确率低的问题,本文提出了一种基于CNN和Attention融合的语义理解模型.该模型基于“编码-解码”框架构建“意图-槽位”联合模型,利用融合意图识别和槽位填充的隐藏语义信息,避免了传统孤立任务的信息遗失问题,实现了端到端的语义理解.同时,在解码过程中引入基于CNN改进的注意力机制来减少原始文本中的冗余信息干扰,从而提升了语义理解的准确率.最后通过将模型应用于电力行业智能客服,通过实验对比验证了提出的融合模型在提升意图识别与槽填充性能上具有较好的效果,可以提升智能客服的用户使用体验.“,”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.