Mode Optimization and Rule Management of Intellectual Property Rights Protection of Educational Resource Data Based on Machine Learning Algorithm

Abstract

Educational resource data are a collection of final documents obtained by users, including full-text journals, books, dissertations, newspapers, conference papers, and other database materials. While searching for information in the educational resource database, these resources also have functions such as copying, downloading, reproduction, and dissemination, which raise the issue of expression and protection of intellectual property. Machine learning takes how computers simulate human learning behaviors as the main research content, which can independently determine learning objects, construct their characteristics, perform additional operations beyond the limitations of preset instructions, and discover value from the expression of relative works. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of intellectual property expression and protection of educational resource data; elaborated the development background, current status, and future challenges of machine learning technology; introduced the methods and principles of data classification algorithm and protection authority identification; performed the technical framework design and expression system establishment of the intellectual property expression of educational resource data based on machine learning; analyzed the mode optimization and rule management of intellectual property protection of educational resource data based on machine learning; and finally conducted a simulation experiment and its result analysis. The results show that the machine learning technology can build a subject-oriented, highly integrated, and time-changing educational resource data storage environment; the comprehensive, analysis-oriented decision-supporting system formed by machine learning can give full play to the potential role of data integration and value discovery and is therefore of great significance for the intellectual property expression and protection of integrated and complexly-related educational resource data. The study results of this paper provide a reference for further research on the intellectual property expression and protection of educational resource data based on machine learning.