Research on Network Space Asset Data Aggregation and Fusion Technology Based on Neural Network Algorithm

This study proposes a technical solution based on neural network algorithms to address the challenges faced by the aggregation and fusion of asset data in cyberspace. This technology effectively improves data integrity and processing efficiency through deep learning models, which has significant implications for enhancing network security protection. The article first outlines the importance of data aggregation and fusion technology for cyberspace assets, then analyzes the shortcomings of existing technologies and proposes improvement strategies. The study provides a detailed introduction to the proposed neural network-based technology method, including the selection, design, construction, optimization, and training process of neural networks. The experimental section validated the performance of the proposed method on multiple publicly available datasets, and the results showed that it outperformed existing techniques in terms of data integrity, accuracy, and processing efficiency. This study provides a new technical perspective for the field of network security and has important value for information security practice.