Research and Analysis of Real-World Asset Token Pricing Model Based on Machine Learning

This article aims at providing a methodical review and brief of the application of machine learning. Especially reviewed tokens RWA assets pricing on blockchain, based on systematic methodology. First, this paper clarifies both core definitions and scenarios of RWA tokenization, asset pricing, and machine learning models in this research. Next, this paper review current research findings in blockchain finance, traditional pricing models, and machine learning pricing methods, identifying the strengths and weaknesses of existing work. This paper summarizes the main model labels in existing research, particularly exploring supervised learning, unsupervised learning, and deep learning in area of asset pricing of tokenization. Then this paper identifies weakness in existing articles, provides a conceptual framework, and suggests future research questions and directions. Finally, discuss the practical implications and potential risks of these models for market participants, platform operators, and regulators, providing a methodological framework and theoretical foundation for subsequent empirical and model development research.