Abstract: In the era of intelligent systems interacting with the physical world, embodied intelligence has reshaped the artificial intelligence research paradigm through a closed-loop mechanism of perception, cognition and action. This has become the core driving force behind innovations in human-machine integration technology. The vision-language-action (VLA) model is a key technology that uses multimodal neural encoding techniques to create a collaborative pathway between visual representation, semantic understanding and decision-making for actions. This lays the groundwork for embodied intelligent agents to perform various tasks in dynamic and complex environments. Nevertheless, the VLA model faces four significant challenges: first, insufficient cross-modal semantic alignment accuracy; second, limited task generalization capability; third, restricted real-time response efficiency, and fourth insufficient training data completeness. As the technology matures, the VLA model will achieve technological breakthroughs in areas such as unified modelling of multimodal fusion, adaptive generalization learning, real-time decision optimization and data-driven closed-loop training. Through deep integration of multimodal cognition and control, the VLA model plays an architectural role in the evolution of general embodied intelligence, providing critical support for the transformation of artificial intelligence from virtual computing to physical applications and holding profound theoretical and practical significance.