Abstract: Now the attention mechanism has been widely used in many fields of deep learning. The structural model based on the attention mechanism can not only record the positional relationship between information, but also measure the importance of different information features based on the weight of the information. Through the selection of relevant and irrelevant information features, dynamic weight parameters are established to strengthen key information and weaken useless information, thereby improving the efficiency of deep learning algorithms and improving some of the defects of traditional deep learning. Therefore, some algorithm structures combined with the attention mechanism are introduced from different applications in the field of image processing, natural language processing, and data prediction, and the attention mechanism-based transformer and reformer algorithms that have been popular in recent years are reviewed. In view of the importance of attention mechanism, the research development of attention mechanism is reviewed, the current development status of attention mechanism is analyzed, and the feasible research directions of this mechanism in the future are discussed.