The Research on Local Cultural IP Network Traffic Prediction Model Based on Ant Colony Algorithm

Abstract

The increasing prominence of localized cultural intellectual property (IP) in digital ecosystems underscores the necessity for advanced network traffic prediction models to ensure stable and efficient performance. Traditional methods, while foundational, often fail to address the complexities inherent in modern network traffic, such as non-linearity, multi-scale temporal patterns, and spatial dependencies across nodes. These limitations result in suboptimal predictions, particularly under dynamic traffic conditions influenced by external factors like user behavior and network configurations. To address these challenges, this study introduces a novel predictive framework leveraging the Ant Colony Algorithm for localized cultural IP network traffic forecasting. By integrating Adaptive Temporal-Spatial Network (ATSN) architecture and the Dynamic Traffic Adaptation Strategy (DTAS), our model dynamically captures both temporal and spatial traffic correlations while ensuring adaptability to abrupt changes. The proposed method incorporates graph neural networks and recurrent structures to optimize spatial- temporal learning, along with hierarchical multi-scale modeling and anomaly-resilient mechanisms for robust performance. Empirical results demonstrate significant improvements in prediction accuracy, scalability, and computational efficiency compared to conventional approaches. These findings not only contribute to the theoretical advancement of network traffic modeling but also offer practical solutions for localized IP management in dynamic, real-world settings.