Research and implementation of text classification method for customer service orders based on multi-model fusion

Liang ZHANG; Xiaoju DAI; Rong ZHENG; Tongze He

doi:10.11959/j.issn.1000-0801.2021236

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Research and implementation of text classification method for customer service orders based on multi-model fusion

Research and Development | 更新时间：2024-06-05

- Research and implementation of text classification method for customer service orders based on multi-model fusion
- Telecommunications Science Vol. 37, Issue 11, Pages: 86-96(2021)
- 作者机构：
  
  1. 上海理想信息产业（集团）有限公司，上海 201315
  2. 北京邮电大学，北京 100876
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021236
  CLC： TP391.1
- Published Online：2021-11，
  
  Published：20 November 2021
- 稿件说明：
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Liang ZHANG, Xiaoju DAI, Rong ZHENG, et al. Research and implementation of text classification method for customer service orders based on multi-model fusion[J]. Telecommunications science, 2021, 37(11): 86-96.
DOI：

Liang ZHANG, Xiaoju DAI, Rong ZHENG, et al. Research and implementation of text classification method for customer service orders based on multi-model fusion[J]. Telecommunications science, 2021, 37(11): 86-96. DOI： 10.11959/j.issn.1000-0801.2021236.

摘要

电信呼叫中心客服在人工进行工单分类时存在归档耗时长、效率低、准确率难以保障的问题，但此场景下类别数量多，且类别间具有层级关联，导致传统文本分类方法准确率较低。针对此问题，提出了一种基于多模型融合的文本分类方法，根据不同层级的数据特点使用不同模型进行分类，考虑了类别的层级关联以提升准确率，并验证了此方法的有效性，可以优化客服生产系统运营流程，加快现场人工客服响应能效，提升客服热线整体运营效率，实现人工智能注智生产。

Abstract

Due to the large amount of order categories and their hierarchical associations

traditional manual order classification method of customer service in telecom call center has the problems of long archiving time

low efficiency and unsustainable accuracy.To solve this problem

a novel text classification algorithm based on multi-model fusion was proposed

which intelligently classify orders with multiple models based on data characteristics and their hierarchical associations

the effectiveness of this method was verified.The current manual operation process was optimized and operation efficiency was enhanced

which support the intelligent transformation and upgradation of existing customer service system.

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Keywords

references

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