Researchers propose the Affect-Prototype-Conditioned Fusion (APCF) framework for open-vocabulary multimodal emotion recognition. The framework extends modal contribution learning to scenarios guided by arbitrary emotional semantics, using an affect-prototype library to model multimodal contribution characteristics. Experiments show APCF outperforms state-of-the-art baselines on two datasets.