Research on the Collaborative Optimization of Creative Communication Models in Cross-Platform Media Ecosystems

In the face of cross-platform media access in the cross-platform media environment, the problem of algorithm-pushed creative diffusion appears to be confronted with the dilemma of content fragmentation and overlap: the same platform experiences semantic fragmentation due to separate optimization on different platforms, and different user behaviors make it hard to find a single strategy to gain cross-media synergy. Firstly, this study builds a heterogeneous information network representation framework, and contents, interactions and recommendations are represented as transferable temporal graphs; Secondly, an adversarial domain adaptation strategy is introduced to generate platform-aware variants while preserving fundamental symbols to balance semantic consistency and form localization; Finally, a reinforcement learning scheduling mechanism is designed to regulate distribution sequence and resource allocation in real time according to user overlap and decay period. Experiments show that, compared with before, the mean crossdomain semantic consistency is increased by 0.86 - 0.62, duplicated reach is improved by 22.4%, and long tail discussion half-life is prolonged from 7.3 days to 15.4 days. These results confirm that hierarchical collaborative optimization could bridge the gap of dissemination discontinuity and effectiveness diffusion.