Design and Implementation of Intelligent Digital Media Interaction System Based on 6G Network Slicing

Rapid growth in intelligent digital media interaction systems (IDMIS) has created new difficulties in controlling and optimizing content distribution and engagement, especially with the impending 6G networks. The purpose of the investigate is to create an intelligent system that uses 6G network slicing to increase digital media communication and user experience through seamless connectivity, dynamic content distribution, and real-time engagement. The structure includes a dynamic, multilayered architecture for IDMIS, and network capital is allocated through 6G network slicing based on user demand and content type. The system includes machine learning (ML) algorithms that predict user behavior and optimize media delivery in real time. To correctly predict user behavior, the research gathers data that capture users' performance and preference (historical interaction data, demographics, contextual data, and user feedback). Once collected, data are processed to reduce dimensionality using principal component analysis (PCA). Refined Support Vector Machine Integrated with Flying Fox Optimization (RSVM-FFO) predicts user behavior and optimizes media delivery in real time. Metrics are used to evaluate the RSVM-FFO approach, such as F1-score (98.12%), accuracy (98.59%), precision (98.57%), and recall (98.17%). The results reveal that the suggested systems considerably improve media interaction effectiveness by reducing latency and bandwidth usage while providing a highly responsive user experience. Finally, advancement in the delivery of high-performance, customized media services is the combination of an IDMIS with 6G network slicing.