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In response to the problems of insufficient multimodal feature extraction, semantic feature differences between modalities, and weak interactions in MSA, a text\u2010centered fusion network with multilevel attention was proposed. We design a deep temporal feature extraction network to extract deep contextual temporal features from each modality. In addition, we develop a text\u2010centered multilevel attention interaction network for deep information interaction guided by the text, including a text\u2010centered cross\u2010modal transformer and an attention\u2010on\u2010attention transformer. We validate our model on two public datasets, CMU\u2010MOSI and CMU\u2010MOSEI. 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