Abstract: The present disclosure provides a method for multimodal emotion classification based on modal space assimilation and contrastive learning. The present disclosure introduces the concept of assimilation. A guidance vector composed of complementary information between modalities is utilized to guide each modality to simultaneously approach a solution space. This operation not only further improves the efficiency of searching for the solution space but also renders heterogeneous spaces of three modalities isomorphic. In a process of making spaces isomorphic, contributions of a plurality of modalities to a final solution space can be effectively balanced to a certain extent. When guiding each modality, this strategy enables a model to be more concerned about emotion features, thereby reducing intra-modal redundancy. Thus, the difficulty of establishing a multimodal representation is reduced.