Multimodal models, which are based on vast data and powerful algorithms, rely on complex structural frameworks. They demonstrate core features such as cross-domain data processing capabilities and the continuous generation of innovative data when they are applied in the knowledge embedding process. The advanced intelligence in knowledge data organization, adaptive multi-scenario knowledge expression, and dynamically coordinated knowledge sharing collectively advance the logic of knowledge generation from understanding existing information to producing entirely new knowledge. However, these models are increasingly revealing issues such as the lack of legitimate and reliable knowledge data sources, opaque and unexplainable generation processes, and inadequate high-quality content output. These issues urgently need to be addressed by developing value-aligned generative models, enhancing controllable generation technologies, refining human feedback mechanisms, and establishing rule-based regulatory systems, to better support knowledge generation and accelerate the development of innovation-driven new forms of productivity.