Optimising news dissemination pathways in the media convergence era: an interactive digital media technology approach

In the era of media convergence, traditional news dissemination systems confront dual challenges of information entropy overload and diminished user engagement. This research proposes an intelligent propagation path optimisation framework leveraging interactive digital media, which integrates multimodal data perception with dynamic user behaviour feedback to establish a dual-layer reinforcement learning decision model. The methodology comprises three core components: a dynamic user interest quantification model combining temporal attention mechanisms with deep feature extraction, a Q-value iteration mechanism with entropy-constrained adaptive learning rate optimisation, and a multi-objective Pareto-optimal framework balancing coverage and timeliness under resource constraints.