  This study addresses the challenges of multi-label text classification. The
difficulties arise from imbalanced data sets, varied text lengths, and numerous
subjective feature labels. Existing solutions include traditional machine
learning and deep neural networks for predictions. However, both approaches
have their limitations. Traditional machine learning often overlooks the
associations between words, while deep neural networks, despite their better
classification performance, come with increased training complexity and time.
This paper proposes a method utilizing the bag-of-words model approach based on
the GloVe model and the CNN-BiLSTM network. The principle is to use the word
vector matrix trained by the GloVe model as the input for the text embedding
layer. Given that the GloVe model requires no further training, the neural
network model can be trained more efficiently. The method achieves an accuracy
rate of 87.26% on the test set and an F1 score of 0.8737, showcasing promising
results.
