At present, local universities are facing problems such as outdated teaching models, lagging discipline settings, and weak integration of industry, academia and research, making it difficult to adapt to the rapid development of artificial intelligence (AI). This study explores the path of talent training model reform in local universities under the background of AI. Through literature review and field research, this study analyzes the challenges faced by universities and proposes the necessity of reform. The research methods include case analysis, interviews with education experts and business people, evaluating the fit between the existing curriculum system and AI technology, promoting the integration of industry, academia and research, setting up interdisciplinary innovative courses, and strengthening teacher training and international exchanges. The results show that after the reform, universities have significantly improved in curriculum setting, faculty strength, scientific research level and employment rate. More than 60% of graduates have entered AI-related industries, and the employment rate and course satisfaction have significantly improved. Research shows that strengthening AI course offerings can help local universities enhance their competitiveness in the era of artificial intelligence and provide stronger intellectual support and innovation drive for social and economic development.

Yangxi Li. Reform Path of Talent Training Model in Local Universities under the Background of Artificial Intelligence. International Journal of Big Data Intelligent Technology (2024), Vol. 5, Issue 1: 221-229. https://doi.org/10.38007/IJBDIT.2024.050123.

[1] Ma H, Ding A. Construction and implementation of a college talent cultivation system under deep learning and data mining algorithms[J]. The Journal of Supercomputing, 2022, 78(4): 5681-5696.

[2] Gong X. A Study on Talent Cultivation Strategies for Film and Television Photography and Production Professionals Oriented to Sports Specialty Students in an Interdisciplinary Context[J]. Higher Education and Practice, 2024, 1(3): 69-73.

[3] Li J, Xue E. How talent cultivation contributes to creating world-class universities in China: A policy discourse analysis[J]. Educational Philosophy and Theory, 2022, 54(12): 2008-2017.

[4] Mangue C L D, Gonondo J. Academic culture and talent cultivation: the Chinese experience[J]. Journal of Comparative & International Higher Education, 2021, 13(1): 133-150.

[5] Zheng J, Shi Q. An empirical study on Cross-Border E-Eommerce talent cultivation based on skill gap theory and big data analysis[J]. Journal of Global Information Management (JGIM), 2021, 30(7): 1-32.

[6] Shi J, Xu Q. Construction and Research of Professional Talent Cultivation System of the Second Classroom in Colleges and Universities‐‐Take the Example of Financial Colleges and Universities[J]. World Scientific Research Journal, 2024, 10(2): 72-76.

[7] Wang L, Sun J, Guo Z, et al. Reconstruction and Practice of" Multi-dimensional Driving, Multiple Integration, Multi-step Progressive" Computer Talent Cultivation System[J]. Advances in Education, Humanities and Social Science Research, 2024, 12(1): 188-188.

[8] Lan Z, Wang L, Zhang Y, et al. " Dual-Agent, Six-Dimensional, Four-Drive" Talent Cultivation Model Innovation and Practice[J]. Advances in Education, Humanities and Social Science Research, 2024, 12(1): 137-137.

[9] Qing S. Applied Ageing Adaptation for Older Communities Based on the Concept of New Development Patterns History of Talent Cultivation Research[J]. Academic Journal of Humanities & Social Sciences, 2023, 6(24): 145-148.

[10] Yi Z. Research On Talents’Behavioral Intentions To Participate Collaborative Talent Cultivation In Chengdu-Chongqing Economic Circle[J]. Journal of Suvarnabhumi Institute of Technology (Humanities and Social Sciences), 2023, 9(1): 273-285.

[11] Cetinic E, She J. Understanding and creating art with AI: Review and outlook[J]. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2022, 18(2): 1-22.

[12] Li Z, Liu F, Yang W, et al. A survey of convolutional neural networks: analysis, applications, and prospects[J]. IEEE transactions on neural networks and learning systems, 2021, 33(12): 6999-7019.

[13] Maulud D, Abdulazeez A M. A review on linear regression comprehensive in machine learning[J]. Journal of Applied Science and Technology Trends, 2020, 1(2): 140-147.

[14] Banitalebi Dehkordi A, Soltanaghaei M R, Boroujeni F Z. The DDoS attacks detection through machine learning and statistical methods in SDN[J]. The Journal of Supercomputing, 2021, 77(3): 2383-2415.

[15] Liao B, Liao H. On the development path of industry university research integration in applied undergraduate colleges from the perspective of emotion regulation and symbiosis theory[J]. International Journal of Neuropsychopharmacology, 2022, 25(Supplement_1): A58-A58.