The invention discloses a Bi-LSTM label recommendation method based on an attention mechanism. The Bi-LSTM label recommendation method based on the attention mechanism comprises the following steps: collecting an experimental data set; analyzing specific text data from the experimental data set; preprocessing the text data; extracting semantic features from the preprocessed question text description; constructing a multi-label classification model; recommending a proper label for the new problem through the constructed multi-label classification model; and evaluating and analyzing a label recommendation result. The method has the beneficial effects that the label recommendation task is mainly converted into the multi-label classification problem through the Bi-LSTM model based on the attention mechanism, the labels are automatically recommended according to the text description content of the problem, and the label recommendation accuracy is improved.