Aiming at the problem that the existing multimodal aspect-based sentiment analysis models have weak correlation between different modalities and it is difficult to effectively utilize the relevant information between them, a multimodal aspect-based sentiment analysis based on prompt learning and attention mechanism was proposed. Firstly, before the text and image features were respectively input into the multi-layer Transformer layer, prompt information was added to the text features and projected into the image features through the coupling function. Secondly, the cross-modal attention mechanism was used for feature fusion, and the irrelevant information in the fusion process was filtered out through BiGRU (bidirectional gated recurrent unit). Then, an attention module guided by aspect information was constructed to strengthen the attention to aspect information. Finally, the sentiment classification results were obtained through the fully connected layer and the softmax classification layer. The model was experimented on the public Twitter-15 and Twitter-17 datasets respectively. The results show that compared with multiple baseline models, the accuracy and F₁ value of the proposed model are improved, which proves the effectiveness of the proposed model in the multimodal aspect-based sentiment analysis task.