God class refers to certain classes that have assumed more than one functionality, which obey the single responsibility principle and consequently impact on the maintainability and intelligibility of software system. Studies, detection and refactoring included, of god class have always attracted research attentions because of its commonness. As a result, a neural network based detection approach is proposed to detect god class code smell. This detection technology not only makes use of common metrics in software, but also exploits the textual information in source code, which is intended to reveal the main roles that the class plays through mining text semantics. In addition, in order to solve the massive labeled data required for supervised deep learning, an approach is proposed to construct labeled data based on open source code. Finally, the proposed approach is evaluated on an open source data set. The result of evaluation shows that the proposed approach outperforms the current method, especially the recall has been greatly improved by 35.58%.