Research on intelligent interaction methods and systems based on the metaverse

This paper aims to construct an efficient and natural intelligent interaction system, and deeply explores key technologies such as multi-modal feature fusion, cross modal alignment, semantic mapping and genuine intention calculation. The research adopts feature - level and decision - level fusion strategies to effectively integrate multi-modal input signals such as images, speech, and text. Moreover, the cross modal alignment and semantic mapping technologies are introduced to ensure the consistency of information from different modalities at the temporal, spatial, and semantic levels. Experimental results show that this intelligent interaction system exhibits excellent performance in processing multi-modal input signals, identifying user intentions, and generating interaction content. The research indicates that the proposed methods are effective and feasible, providing strong support for the construction of intelligent interaction systems.