基于大模型的AI通话分析智能体研究与实现

Research and Implementation of AI Voice Call Analysis Agent Based on Large Model

摘要: 随着国家“数据要素×”战略的深入推进，如何释放企业海量语音通话数据的潜在价值成为关键课题。针对当前企业在语音数据利用中存在的采集渠道分散、处理模式低效及语义挖掘浅层化等瓶颈，本文提出并实现了一种基于运营商合规数据的AI通话分析智能体。该智能体依托联通云犀平台与元景大模型，构建了“智能体实时动态调度CoE (Collaboration of Experts)”引擎和AI通话分析智能体。CoE引擎通过任务规划与多模型混合调度机制，实现了从语音采集、高精度转写、多视角语义理解到结构化价值输出的全链路自动化。AI通话分析智能体实现了基于利益相关者理论的多视角标签体系，以适应不同业务角色的分析需求。对比实验与实证分析结果表明，本文提出的方法在关键意图识别的F1分数上显著优于传统基线模型；在某物流企业的实际应用中，推动订单揽收率从62%提升至92%，投诉处理时效由48小时压缩至15分钟。本文方法为企业通信数据要素的资产化与市场化流通提供了可复制的技术范式。

Abstract: With the deepening implementation of the national “Data Elements×” strategy, unlocking the latent value of massive enterprise voice call data has become a critical imperative. Addressing the bottlenecks in current data utilization—such as fragmented collection channels, inefficient processing modes, and shallow semantic mining—this paper proposes and implements an AI-powered voice call analysis agent based on compliant telecom operator data. Leveraging the China Unicom Yunxi Platform and the Yuanjing Large Model, this paper constructs an “Intelligent Agent Real-time Dynamic Scheduling CoE (Collaboration of Experts)” engine and an AI-powered voice call analysis agent. The CoE engine implements task planning and multi-model hybrid scheduling mechanisms. This engine achieves end-to-end automation encompassing voice collection, high-precision transcription, multi-view semantic understanding, and structured value output. The AI-powered voice call analysis agent constructs a multi-view tagging system based on stakeholder theory, which is introduced to adapt to the analytical needs of various business roles. Comparative experiments and empirical analysis demonstrate that the proposed solution significantly outperforms traditional baseline models in the F1 score for key intent recognition. In a practical application within a logistics enterprise, the solution drove an increase in the order pickup rate from 62% to 92% and reduced customer complaint resolution time from 48 hours to 15 minutes. This research provides a replicable technical paradigm for the assetization and market circulation of enterprise communication data elements.