Hierarchical Holographic Modeling of Network Ideology Risks
Qian Chen · International Journal of Cognitive Informatics and Natural Intelligence · 2024
The rapid evolution of computer network technology and the proliferation of social information have elevated the role of computer networks in various domains. However, this progress has also introduced new risks to network ideology security within the big data environment. Using a hierarchical holographic modeling method, this study identifies three key risks—subject-related, information-related, and environmental risks—and constructs a comprehensive three-level index framework for evaluating network ideology risks. By integrating network social governance with the complexities of the big data era, the study proposes an optimized pathway for network social governance, enriching traditional research approaches. The introduction of the LR-NSRPM method reduces CPU and RAM utilization by 21.65% compared to existing methodologies. Additionally, the study outlines observable risk indicators and high-risk elements within network ideology, providing valuable insights for managing network ideology risks in the big data environment.