A Novel Document Labeling Model and Web3.0-Based Academic Papers Platform

This paper introduces a novel academic paper framework, leveraging the integration of Web3.0, blockchain, and metaverse technologies, alongside advanced natural language processing (NLP) techniques, to address the challenges in handling the growing volume and diversity of the existing works. In this paper we propose a solution emphasizes enhanced data security, transparency, automation, and improved user experience through decentralized, encrypted, and tamper-proof storage, ensuring the integrity and authenticity of academic content. The framework's innovative use of smart contracts automates tedious manual operations, i.e., copyright licensing and peer review processes, enhancing efficiency and reducing human errors. Furthermore, our model incorporates a unique algorithm, the BiBI-LSTM-CNN-Apriori, aim to optimize document tagging and classification through a combination of bi-directional long short-term memory (BI-LSTM), convolutional neural network (CNN), and Apriori layers. Our algorithmic approach facilitates intelligent recommendation functionalities and supports the automated organization and categorization of academic works.