The profound impact and trend of deepseek on literary content production and consumption patterns in the digital culture industry
DOI:
https://doi.org/10.52152/800040Abstract
In order to promote the development of artificial intelligence technology and literature towards a good trend, this paper analyzes the impact of DeepSeek technology on literature around technology, literary creation, literary consumption, and the digital culture industry, etc. DeepSeek technology has realized a new paradigm shift by virtue of extreme engineering optimization, vertical and segmented technological paths, and widespread popularization of technological value. DeepSeek technology has realized a new paradigm shift by virtue of its extreme engineering optimization, vertical and segmented technological paths, and widespread popularization of technological values, forming a unique path mechanism covering information decoding, symbolic performance, negotiation and calibration, and heterogeneous symbiosis. This mechanism has triggered changes in the quantitative change, qualitative change, diffusion and gatekeeping of knowledge, and also stimulated new demands for literary consumption and improved the supply capacity of literary consumption. The article proposes that in the future, it is necessary to strengthen the construction of a high-end platform for knowledge production and service based on AIGC technology, so as to promote knowledge innovation. In addition, intellectual property technology and institutional protection system should be constructed, regulation of misleading knowledge generation and dissemination should be strengthened, and AIGC algorithmic literacy capacity should be enhanced, etc. These suggestions can provide meaningful references and insights for researchers and practitioners in the field of digital culture industry.
References
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., ... & Zitnik, M. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47-60.
Huynh-The, T., Pham, Q. V., Pham, X. Q., Nguyen, T. T., Han, Z., & Kim, D. S. (2023). Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117, 105581.
Poo, M. M. (Ed.). (2025). Reflections on DeepSeek's breakthrough. National Science Review, 12(3), nwaf044.
Sallam, M., Al-Mahzoum, K., Sallam, M., & Mijwil, M. M. (2025). DeepSeek: Is it the End of Generative AI Monopoly or the Mark of the Impending Doomsday?. Mesopotamian Journal of Big Data, 2025, 26-34.
Krause, D. (2025). DeepSeek and FinTech: The Democratization of AI and Its Global Implications. Available at SSRN 5116322.
Kumar, P. R. V. (2025). Revolutionizing the Search Game: How Deepseek is Transforming Information-Seeking Behavior in the Digital Age. International Journal of Scientific Research in Engineering and Management (IJSREM)
IS, S., & DR, A. (2025). Advancements in AI-Powered NLP Models: A Critical Analysis of ChatGPT and DeepSeek. Available at SSRN 5125445.
Rahman, A., Mahir, S. H., Tashrif, M. T. A., Aishi, A. A., Karim, M. A., Kundu, D., ... & Eidmum, M. D. (2025). Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects. arXiv preprint arXiv:2503.04783.
Xu, L., & Mani, M. (2025). AI-Enhanced Literary Education: Unveiling the Potential of Generative AI in Literary Education. International Journal of Academic Research in Business and Social Sciences, 15(1), 1780-1792.
Dillon, S., & Schaffer-Goddard, J. (2023). What AI researchers read: the role of literature in artificial intelligence research. Interdisciplinary Science Reviews, 48(1), 15-42.
Moulaison-Sandy, H., Castaño-Muñoz, W., Ridenour, L., & Adkins, D. (2025). AI literature review systems: an analysis of performance, affordances, and outputs for a complex topic in the social sciences. Information Research an international electronic journal, 30(iConf), 1244-1252.
Wang, K., Lu, L., Fang, J., Xing, Y., Tong, Z., & Wang, L. (2023). The downside of artificial intelligence (AI) in green choices: How AI recommender systems decrease green consumption. Managerial and Decision Economics, 44(6), 3346-3353.
Oyekunle, D. O. T., Okwudili Matthew, U., Preston, D., & Boohene, D. (2024). Trust beyond technology algorithms: A theoretical exploration of consumer trust and behavior in technological consumption and AI projects. Journal of Computer and Communications, 12(06), 10-4236.
Naeem, M. F., Arshad, R., & Saleem, H. (2025). AI in Production, Consumption and Regulation of Media: A Case Study Analysis. Qlantic Journal of Social Sciences and Humanities, 6(1), 107-115.
Carvalho Souza, M. E., & Weigang, L. Grok, Gemini, ChatGPT and DeepSeek: Comparison and Applications in Conversational Artificial Intelligence. INTELIGENCIA ARTIFICIAL, 2, 1.
Jiang, Q., Gao, Z., & Karniadakis, G. E. (2025). DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks. Theoretical and Applied Mechanics Letters, 100583.
Lu, H., Liu, W., Zhang, B., Wang, B., Dong, K., Liu, B., ... & Ruan, C. (2024). Deepseek-vl: towards real-world vision-language understanding. arXiv preprint arXiv:2403.05525.
Parmar, M., & Govindarajulu, Y. (2025). Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies. arXiv preprint arXiv:2501.17030.
Allen, R. (2025). DeepSeek and AI innovation: How Chinese universities broke through the glass ceiling of technological advancement. American Journal of STEM Education, 6, 1-10.
Zhu, Q., Guo, D., Shao, Z., Yang, D., Wang, P., Xu, R., ... & Liang, W. (2024). Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence. arXiv preprint arXiv:2406.11931.
Ratten, V., Hasan, R., Kumar, D., Bustard, J., Ojala, A., & Salamzadeh, Y. (2024). Learning from artificial intelligence researchers about international business implications. Thunderbird International Business Review, 66(2), 211-219.
Yokoyama, A. M., Ferro, M., de Paula, F. B., Vieira, V. G., & Schulze, B. (2023). Investigating hardware and software aspects in the energy consumption of machine learning: A green AI‐centric analysis. Concurrency and Computation: Practice and Experience, 35(24), e7825.
Desislavov, R., Martínez-Plumed, F., & Hernández-Orallo, J. (2023). Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems, 38, 100857.
Chen, X., Hu, Z., & Wang, C. (2024). Empowering education development through AIGC: A systematic literature review. Education and Information Technologies, 29(13), 17485-17537.
Wu, J., Cai, Y., Sun, T., Ma, K., & Lu, C. (2024). Integrating AIGC with design: dependence, application, and evolution-a systematic literature review. Journal of Engineering Design, 1-39.
Wang, X., Hong, Y., & He, X. (2024). Exploring artificial intelligence generated content (AIGC) applications in the metaverse: Challenges, solutions, and future directions. IET Blockchain, 4(4), 365-378.
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