Research on Accurate Training Mechanism of Digital Talents for Higher Vocational Innovation and Entrepreneurship Education under Industry-Teaching Integration Driven by Big Data

In this paper, text mining technology is utilized to construct talent portrait labeling system, and LDA topic model is used to mine talent information dimensions. After that, the fuzzy C-mean clustering algorithm (FCM) is used to cluster and analyze the data. Finally, the digital talent group portrait of higher vocational innovation and entrepreneurship education based on FCM is constructed to implement the precise cultivation method. In this paper, students related to innovation and entrepreneurship in higher vocational colleges are clustered into four categories: to be strengthened, balanced development, skill-deficient, and imaginative. The weights of theoretical knowledge dimension, practical ability, professional skills, brainstorming and competitiveness dimension are 0.0970, 0.5796, 0.097, 0.1212 and 0.1216, respectively. 2022 spring semester, the impact of “hybrid teaching” variable on innovation ability, entrepreneurship ability and knowledge acquisition are less than 2021, while the impact of “teamwork” variable on innovation ability, entrepreneurship ability and knowledge acquisition are higher than 2021, and the impact of “experimental design” variable on knowledge acquisition is also higher than 2021. The effects of “teamwork” variable on innovation ability, entrepreneurship ability and knowledge acquisition in the spring semester of 2022 are lower than those in 2021, while the effects of “experimental design” variable on knowledge acquisition are higher than those in 2021, so that the effects of this paper’s teaching and traditional teaching on the cultivation of innovative and entrepreneurial digital talents are different. The effects of this paper’s teaching and traditional teaching on the innovation and entrepreneurial cultivation of digital talents are different.