Research on the Optimization of Digital Object System by Integrating Metadata Standard and Machine Learning Algorithm

Digital object system, as one of the core architectures for the future development of the Internet, has the advantage of allocating unique identifiers to each data object to improve data management efficiency and interoperability. However, the domestic digital object system still faces many challenges in the accumulation of application scenarios and standardization construction. In order to solve these problems, this paper proposes an optimization scheme combining metadata standards and machine learning algorithms. Firstly, by analyzing existing technologies, a unified digital object generation and management framework is constructed, and the creation and management process of digital objects is standardized by combining international metadata standards, which provides a foundation for the standardization and development of digital object system. In this paper, data diffusion strategy and improved Bayesian network algorithm are adopted to solve the problem of insufficient test data. By expanding the size of sample data set and combining with expert knowledge, a large number of standards-compliant and highly realistic data sets are generated, which provide sufficient support for performance testing. This paper verifies the effectiveness of the method in the encapsulation and generation of digital objects, and further confirms the practicability and reliability of the method through the verification. Finally, a loosely coupled data management platform is designed to efficiently integrate data, models and algorithms, which not only improves the operability of the digital object system, but also provides technical support for its future development. The research in this paper provides an innovative solution for the standardization, data generation and performance evaluation of digital object system, which has significant theoretical value and practical significance.