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China's exploration of the legal application of big data is still far from thorough enough. This article proposes a universal suitable for hierarchical big data storage systems, which can quickly add new cache policies when needed and provide cache scheduling strategies. The neural training algorithm used in legal information systems implements a complete parallel computing framework for large-scale neural network training, supporting distributed storage and management of large-scale sample data. The experimental results show that the framework has good scalability and fault tolerance, and can quickly train legal information systems, improving their efficiency and response speed. This provides new ideas and methods for the design and development of legal information systems.
Ying Wang (Mon,) studied this question.