Multimodal sentiment analysis is a task that uses subjective information from multiple modalities to analyze sentiment. Exploring how to effectively learn the interaction between modalities has always been an essential task in multimodal analysis. In recent research, it is found that the learning rate of different modalities is unbalanced, leading to the convergence of one modality while the rest of the modalities are under-fitting, which weakens the effect of multimodal collaborative decision-making. In order to combine multiple modalities more effectively and learn the multimodal sentiment features with rich expression, this study proposes a multimodal sentiment analysis method based on adaptive weight fusion. The method of adaptive weight fusion is divided into two phases. The first phase is to adaptively change the fusion weights of unimodal feature representations according to the difference of unimodal learning gradients to dynamically balance the modal learning rate. The study calls this phase balanced fusion (B-fusion). The second phase is to eliminate the impact of the fusion weights of B-fusion on task analysis, propose the modal attention to explore the contributions of modalities to the task, and dynamically allocate the fusion weight to each modality. The study calls this phase attention fusion (A-fusion). The experimental results show that the introduction of the B-fusion method into existing multimodal sentiment analysis methods can effectively improve the accuracy of sentiment analysis. The ablation experiment results show that adding the A-fusion method to B-fusion can effectively reduce the impact of B-fusion weights on the task, which is conducive to improving the analysis results of sentiment analysis. Compared with the existing multimodal sentiment analysis models, the proposed method has a simpler structure, lower computational consumption, and better task accuracy than these comparison models, which shows that the method has high efficiency and excellent performance in multimodal sentiment analysis tasks.