Application and Optimization of Natural Language Processing Technology in Intelligent Customer Service System
DOI:
https://doi.org/10.53469/jtpms.2025.05(4).02Keywords:
Natural language processing technology, Intelligent customer service system, Application and OptimizationAbstract
In today's global digital transformation wave, it is increasingly urgent for enterprises to pursue efficient and accurate customer service. With the expansion of business scale and the diversification of customer needs, traditional customer service models have gradually exposed problems such as slow response speed, low processing efficiency, and uneven service quality, which are difficult to meet the customer service needs of modern enterprises. In this context, the introduction of natural language processing technology has injected new vitality into the development of intelligent customer service systems. NLP technology simulates human language processing ability through advanced technologies such as deep learning and machine learning, enabling computers to accurately understand and generate natural language, greatly improving the interaction ability and service efficiency of intelligent customer service systems. The introduction of this technology not only provides more convenient and efficient customer service solutions for enterprises, but also provides strong support for their digital transformation and intelligent upgrading.
References
Chen, J., Zhang, X., Wu, Y., Ghosh, S., Natarajan, P., Chang, S. F., & Allebach, J. (2022). One-stage object referring with gaze estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5021-5030).
Wang, Y., Shen, Z., Hu, K., Yang, J., & Li, C. (2025). AI End-to-End Autonomous Driving.
Chew, J., Shen, Z., Hu, K., Wang, Y., & Wang, Z. (2025). Artificial Intelligence Optimizes the Accounting Data Integration and Financial Risk Assessment Model of the E-commerce Platform. International Journal of Management Science Research, 8(2), 7-17.
Yin, Y., Xu, G., Xie, Y., Luo, Y., Wei, Z., & Li, Z. (2024). Utilizing Deep Learning for Crystal System Classification in Lithium - Ion Batteries. Journal of Theory and Practice of Engineering Science, 4(03), 199–206. https://doi.org/10.53469/jtpes.2024.04(03).19
Luo, Y., Wei, Z., Xu, G., Li, Z., Xie, Y., & Yin, Y. (2024). Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization. Journal of Theory and Practice of Engineering Science, 4(02), 52–57. https://doi.org/10.53469/jtpes.2024.04(02).08
Yan, H., Wang, Z., Xu, Z., Wang, Z., Wu, Z., & Lyu, R. (2024, July). Research on image super-resolution reconstruction mechanism based on convolutional neural network. In Proceedings of the 2024 4th International Conference on Artificial Intelligence, Automation and High Performance Computing (pp. 142-146).
Long, Y., Gu, D., Li, X., Lu, P., & Cao, J. (2024, September). Enhancing Educational Content Matching Using Transformer Models and InfoNCE Loss. In 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE) (pp. 11-15). IEEE.
Tang, Y., & Zhao, S. (2025). Research on Relationship Between Aging Population Distribution and Real Estate Market Dynamics based on Neural Networks.
Wu, W. (2025). Fault Detection and Prediction in Models: Optimizing Resource Usage in Cloud Infrastructure.
Yao, T. (2024, August). Research on the Local Head Loss Coefficient in Short-Tube Hydraulic Testing. In 2024 3rd International Conference on Applied Mechanics and Engineering Structures (AMES 2024) (pp. 89-97). Atlantis Press.
