The integration of big data technology in the manufacturing process has become a norm, and as society's dependence on the digital economy increases, colleges and universities must adjust their teaching methods to cater to their students' needs. In evaluating the success of business-school partnerships, there is a need for common criteria and visualising data analysis results. However, the current educational approach presents some challenges, including a lack of practical experience with software, overemphasis on theoretical concepts, and inadequate training in problem-oriented statistical modeling and big data statistics projects. Industry-education cooperation should be leveraged to enhance the implementation of big data technology and promote its overall development. This paper analyses the shortcomings of traditional talent training models in higher education and proposes incorporating industrial education to address the gaps. The paper aims to bridge the industry-education gap by developing and implementing an Inter-Technology Information Management (ITIM) system for quality education. The ITIM system uses a fuzzy algorithm to evaluate the quality of education and provides various intelligent functional modules, such as group management, financial management, and process-to-process communication. Compared to other integration models, the proposed management system offers superior performance with an industrial education performance accuracy of 98%, an average analysis, and calculation time of 20 ms and a maximum performance efficiency of 98%.By incorporating dynamic analysis of industry education, the experimental results of the talent training model have led to improvements in teaching effectiveness, student learning, and theoretical-applied teaching quality.
Keywords: Big data; Fuzzy algorithm; Industry-education; Inter-technology information management; KNN algorithm.
© 2023 The Authors.