Application of Big Data in Entrepreneurship and Innovation Education for Higher Vocational Teaching

Introduction

Generally, big data is complex as well as extensive in quantity and distribution, making it necessary to adopt new technical methods to process data. Therefore, intelligent analysis technology is of great significance in data processing. Research on the applicability of big data analytics techniques to grid analytics internet teaching in China has increasingly become prominent in innovative and entrepreneurial education in higher educational institutions. As big data analytics technology has developed in China, the intelligent analytics methods for innovative entrepreneurial education in higher education institutions have gradually increased, posing new challenges to the rapidity and generality of intelligent grid analytics teaching methods (Liu, 2021). Therefore, solving the intelligent analysis mode of innovative entrepreneurial education in professional colleges based on grid analysis has long been an essential research challenge in China (Liang et al., 2021). In this context, this paper studies the online grid teaching method for innovative entrepreneurial education in higher vocational institutions in the context of grid analysis. It proposes a grid innovation teaching model for entrepreneurship based on an adaptive identification weighting algorithm.

Entrepreneurship education aims to cultivate students’ entrepreneurial ability and innovative thinking. Compared with traditional discipline-based education, entrepreneurship education has particular characteristics and unique teaching methods. Firstly, entrepreneurship education focuses on interdisciplinary teaching content, covering a wide range of disciplines, such as business management, marketing, and finance. Secondly, entrepreneurship education emphasizes a practical orientation, helping students gain real-world entrepreneurial experiences and improve their practical operation ability through various training projects, business plan competitions, and other activities. In addition, entrepreneurship education also emphasizes cultivating students’ risk awareness and innovative thinking to cope with the challenges and uncertainties of entrepreneurship. Therefore, entrepreneurship education significantly enriches students’ comprehension, entrepreneurial spirit, and innovation ability. Through close integration with the entrepreneurial ecosystem, such education provides students with a broader development platform and practice opportunities. It promotes the development of an entrepreneurial culture, innovation, and entrepreneurial activities.

Innovation originates from entrepreneurship education, which can be understood as an “enterprising and pioneering skills education” and is regarded as “the third education passport.” Although innovation and entrepreneurship education cannot presently be regarded as an independent discipline, it has significant attributes of an independent discipline. In order to address the problems of low data reliability, weak result dimensions, and limited application in the existing senior higher education innovation and entrepreneurial education model, this research paper investigates the application of multiple assessment models based on an adaptive identification weighting algorithm to higher education institutions’ innovation and entrepreneurial education (Khaled et al., 2014; Vescio et al., 2008).

In contrast to the disadvantages of the current mainstream multivariate evaluation and optimization analysis models that require pre-processing of specific data, the innovation of this paper is its use of an adaptive recognition weighting algorithm that combines the quantitative evaluation of entrepreneurial innovation in artificial intelligence and image recognition technology. The model is used to construct an online innovative entrepreneurial education method for higher education institutions. On this basis, the model can not only record and store the innovative and entrepreneurial education data of students in different vocational academies but also fully utilize the complementary nature of each individual in innovative and entrepreneurial education. It uses a secondary audit to achieve the intelligent classification of an innovative entrepreneurial education. On the other hand, using quantitative indicators to prioritize an innovative entrepreneurial education can effectively customize the factors affecting such an education, reduce evaluation errors, and improve its intrinsic stability and data reliability.