The ability to understand and predict future events from visual information is one of the core aspects of human cognitive function. Events in videos exhibit dynamic evolution, such as motion continuity, environmental changes, and multi-object interactions. This requires models to comprehend the causal relationships and logical chains of events (e.g., "opening a door" may lead to "entering a room"). This capability compels AI to shift from passive recognition to active reasoning, advancing it toward human-like cognitive levels. To address this challenge, the research team proposes a video future event prediction method based on common-sense knowledge and causal effects. Innovatively, the team employs "chain-of-thought" technology to guide large models in uncovering latent causal relationships within video event sequences, thereby enhancing AI's cognitive abilities. The research findings can be applied to early warnings for potential conflicts or accidents, predicting patient risks, and providing automated plot development support for film production. These applications break through technical bottlenecks in video understanding and enhance the core capabilities of intelligent systems in practical scenarios.