It is a difficult process for developers to use API and API sequences (APIs) correctly in software development. When developers are faced with unfamiliar function libraries or code repositories like Github that contains a large number of APIs, they need assistance of some recommendation tools or system. To the best of our knowledge, DeepApi can better understand the semantics of user’s query, but the RNN-based model has some problems: (1) it does not consider the weight of each word, (2) the input sequence is compressed into a fixed length vector, which loses much useful information, (3) long sentences lead to loss of key information. Therefore, this study uses a model based on attention mechanism to distinguish the importance of each word and solve the problem of long-distance dependence caused by long query input. We crawled 649 Java open source projects from Github and processed them to get a training set of 114 364 pairs of annotation-API sequences. The experimental results show that the proposed method can increase BLUE index by more than about 20% compared with DeepApi method on Top1, Top5, and Top10.