Analysis of DeepSeek’s Core Technology and its Effect on the Artificial Intelligence Area

Jiahua Zhang

doi:10.54254/2755-2721/2025.AST25999

Applied and Computational EngineeringOpen access

Analysis of DeepSeek’s Core Technology and its Effect on the Artificial Intelligence Area

Research Article

Open Access

Analysis of DeepSeek’s Core Technology and its Effect on the Artificial Intelligence Area

Jiahua Zhang ^1*

¹ Shanghai Shangde Experimental School, Shanghai, China, 201315

^*Corresponding author: brucez113@outlook.com

Published on 13 August 2025

ACE Vol.175

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-237-9

ISBN (Online): 978-1-80590-238-6

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Abstract

As recent years AI model developed, various new technology have been created, causing the AI model field to gradually become more competitive day after day, since it was born in 2023, DeepSeek gain benefit and was able to compete with other big companies using its benefits in code and mathematics advancement, localization and Chinese optimization, free to use, high performance. This thesis will analyze DeepSeek’s core technology from its model architecture innovation, technology training and innovation, and performance comparison area. It will also explain DeepSeek’s effect on the artificial intelligence area by focusing on DeepSeek’s technology development in AI technology, the change in the competitive field of AI companies and ethical problems. In this thesis, we find that DeepSeek's ability to become a leader in the field of AI macromodels in just a few years is due to Deepseek's model architecture innovations, training strategy optimizations, and outstanding key performance, but like all other AI models, DeepSeek have limitations while having various benefits, these limitations include problems with the timeliness of data updates, as well as doubts about the accuracy and completeness of data and data biases.

Keywords:

DeepSeek, core technology, AI model

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References

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References

[1]. CAI Rui, GE Jun, and SUN Zhe. Overview of the Development of AI Pre-trained Large Models [J/OL]. Journal of Chinese Mini-Micro Computer Systems, 2024: 1-12.

[2]. Yufeng Wang. Unlocking a New Chapter in AI Large-Scale Model Applications: Technological Evolution, Challenges, and Future Prospects, 2024: 18-19

[4]. Turner, R. E. An introduction to transformers. arXiv preprint arXiv: 2304.10557. 2023

[6]. Masoudnia, S., & Ebrahimpour, R. Mixture of experts: a literature survey. Artificial Intelligence Review, 42, 2014: 275-293.

[7]. Wang, C., & Kantarcioglu, M. A review of DeepSeek models' key innovative techniques. arXiv preprint arXiv: 2503.11486. 2025

[8]. Gu, Z., Zhang, H., Chen, R., Hu, Y., & Zhang, H. Unpacking Positional Encoding in Transformers: A Spectral Analysis of Content-Position Coupling. arXiv preprint arXiv: 2505.13027. 2025

Cite this article

Zhang,J. (2025). Analysis of DeepSeek’s Core Technology and its Effect on the Artificial Intelligence Area. Applied and Computational Engineering,175,71-77.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

ISBN: 978-1-80590-237-9(Print) / 978-1-80590-238-6(Online)

Editor: Marwan Omar, Mian Umer Shafiq

Conference website: https://www.confcds.org

Conference date: 19 August 2025

Series: Applied and Computational Engineering

Volume number: Vol.175

ISSN: 2755-2721(Print) / 2755-273X(Online)

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