Developers often need to use various application programming interfaces (API) to reuse existing software frameworks, class libraries, and so on. Because of the complexity of the API itself, or the lack of documentation, developers often make some API misuses, which can lead to some code defects. In order to automatically detect API misuse defects, the API use specification is required and the API is tested according to the specification. However, API specifications that can be used for automatic detection are difficult to obtain, and the cost of manual writing and maintenance is high. To address the issue, this study applies the recurrent neural network model of deep learning to the task of learning API use specifications and the task of detecting the API misuse defect. In this study, based on a large number of open source Java code, the training sample of API use specification is extracted based on static analysis method, and then use the training sample to set up the recurrent neural network to learning API use specification. On this basis, this study makes a context-based prediction on the API use code, and finds out the potential API misuse defects by comparing the prediction results with the actual code. The method above is implemented, and it is evaluated with experiments about Java encryption related APIs and their used code. The results show that the proposed approach has the ability to a certain extent to automatically detect API misuse defects.