Research on the Innovative Trend of Digital Talent Cultivation Mode Based on Support Machine Vector Algorithm
As rapid digital transformation, implementing the strategy for forming cooperative relationship between schools and enterprise, and realizing industry and education deep integration and the establishment of industrial college, which have become important methods of improving the capability of cultivating digital talents and driving upgrading of the industry. The paper, taking Huawei Digital Technology Modern industrial college established by Huawei company and Nanning University as research object and samples, discusses some new ways and new methods of forming integration of industry and education in the process of cultivating digital talents. The analysis method takes into account both theoretical analysis and case study, and at last combines with the practical case exploration and Support Vector Machine (SVM) algorithm to compare different talent cultivation models and get a conclusion that “ten co” model (such as co-developing training programs and courses) and “five deep integrations” (such as deep integrated resources, team integration, classroom integration, teaching integration and enterprise integration) can effectively narrow down the gaps between educational chains and industrial chains. At last, new concepts of professional cluster matching industry needs and new model of integrated industry and education cluster of professional courses are introduced for further supply, and then some new ways and methods of schools and enterprise cooperation and integration of industrial and education are proposed to solve the problem of developing digital talent cluster of modern college from the perspective of education, which can provide the practical ideas and decision reference to the process of industrial and education integration and cultivation of digital talents in colleges and universities.
Authors
- Xiaomin Hu
- Y. P. Huang
Institutions
- Nanning Normal University (CN)
Publication Details
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1145/3746469.3746503
- Primary Topic
- E-commerce and Technology Innovations
- Type
- article
- Field-Weighted Citation Impact
- 0.00