Application cases of natural language processing technology based on deep learning in intelligent customer service
基于深度学习的自然语言处理技术在智能客服中的应用案例

摘要

基于深度学习的自然语言处理技术在智能客服中的应用案例。本文首先阐述了当前智能客服系统面临的挑战，如理解复杂用户意图和提供准确响应等。接着，详细分析了几种关键的深度学习技术，包括但不限于循环神经网络（RNN）、长短期记忆网络（LSTM）以及Transformer模型，如何改进这些系统的性能。通过实际案例研究展示了这些技术在提高客服效率、增强用户体验方面的潜力。采用深度学习技术可以显著提升智能客服的理解能力和响应质量，为用户提供更加个性化和高效的服务。

Abstract

This paper focuses on the application cases of natural language processing technology based on deep learning in intelligent customer service. Firstly, it expounds on the challenges faced by current intelligent customer service systems, such as understanding complex user intents and providing accurate responses. Then, it conducts a detailed analysis of several key deep learning technologies, including but not limited to Recurrent Neural Network (RNN), Long Short-Term Memory network (LSTM), and Transformer model, and explains how these technologies can improve the performance of such systems. Through practical case studies, the potential of these technologies in enhancing customer service efficiency and improving the user experience is demonstrated. The adoption of deep learning technologies can significantly enhance the understanding ability and response quality of intelligent customer service, and provide users with more personalized and efficient services.