Abstract: When encountered by technical problems, developers often post questions on technical forums such as Stack Overflow, and wait for satisfying answers. QA forums are also an important manifestation of Internet-based group intelligence software development. However, the questions posted in the forums may not get satisfying answers. Therefore, asking problems and passively waiting for solution is not always the best strategy. To this end, we propose a deep neural network based approach to automatically predict whether the questions can obtain satisfying answers. Knowing whether the questions can be effectively answered in advance, developers figure out the best strategy for their technical problems in advance. This approach not only takes full usage of the text information of the problems itself, but also exploits the relevant content of the inquirer of the questions. With the latest deep learning technologies, it fully exploits the intrinsic relationship between the input features and the questions’ solving status. Experimental results on the dataset provided by Stack Overflow suggest that the proposed approach can accurately predict the solving status of the questions. The precision of predicting well-answered problems is 58.87%, and the recall is 46.68% (in contrast, random guess results in a precision of 38.77%, and recall of 35.26%), better than KNN and FastText.