Fraud call identification based on large language models and event evolution graphs

Hu Chengyi

doi:10.11959/j.issn.1000-0801.DXKX250564

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Fraud call identification based on large language models and event evolution graphs

Research and Development | 更新时间：2026-07-21

- Fraud call identification based on large language models and event evolution graphs
- Telecommunications Science Vol. 42, Issue 6, Pages: 203-216(2026)
- 作者机构：
  
  卓望信息技术（北京）有限公司，北京 100072
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX250564
  CLC： TP391
- Received：22 September 2025，
  
  Revised：2025-12-29，
  
  Accepted：10 February 2026，
  
  Online First：21 July 2026，
  
  Published：20 June 2026
- 稿件说明：
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胡程忆.基于大语言模型与事件演化图谱的诈骗电话识别方法[J].电信科学,2026,42(06):203-216.

Hu Chengyi.Fraud call identification based on large language models and event evolution graphs[J].Telecommunications Science,2026,42(06):203-216.
胡程忆.基于大语言模型与事件演化图谱的诈骗电话识别方法[J].电信科学,2026,42(06):203-216. DOI： 10.11959/j.issn.1000-0801.DXKX250564.

Hu Chengyi.Fraud call identification based on large language models and event evolution graphs[J].Telecommunications Science,2026,42(06):203-216. DOI： 10.11959/j.issn.1000-0801.DXKX250564.

摘要

针对电信诈骗话术快速演化、传统方法依赖大量标注样本以及基于大模型的端到端识别存在幻觉风险等问题，提出一种基于大语言模型与事件演化图谱的诈骗电话识别方法。该方法引入单类学习思想，在无需负样本的条件下，利用大语言模型的零样本能力，将主叫话术抽象为标准化的动宾短语事件演化链，通过语义相似度融合构建可增量扩展的事件演化图谱，并采用关键事件节点匹配与连通性检测进行风险判别。实验结果表明，该方法的

1值在2 000条样本规模下达84.84%，在8 000条样本规模下可提升至91.46%，性能随样本规模增长呈现一定的缩放律特征。该方法为识别持续演化的复杂诈骗行为提供了精准且具有扩展性的技术路径。

Abstract

In response to the rapid evolution of fraud call scripts

the reliance on a large number of labeled samples in traditional methods

and the potential hallucination risks in end-to-end recognition based on large models

a fraud call identification method based on large language models and event evolution graphs was proposed. By introducing the concept of one-class learning

the zero-shot capability of large language models was leveraged to abstract caller speech into standardized verb-object phrase event evolution chains without the need for negative samples. An incrementally expandable event evolution graph was constructed through semantic similarity fusion

and risk discrimination was performed using key event node matching and connectivity detection. Experimental results show that the proposed method achieves an

1 score of 84.84% on a sample scale of 2 000 instances

which is further improved to 91.46% on a scale of 8 000 instances

with performance exhibiting certain scaling law characteristics as the sample size increases. The proposed method provides a precise and scalable technical approach for identifying complex and continuously evolving fraudulent behaviors.

关键词

Keywords

references

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