# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 5 Conclusions  
This paper focuses on optimizing word embedding semantic representations and innovating classification model architectures in text classification tasks. We propose a classification model based on improved word embeddings and a hybrid neural network. To address the limitations of traditional Word2 Vec embeddings, which do not consider word frequency, POS, or the varying importance of the same word in different documents, we have designed a novel weighted word vector generation algorithm. By weighting Word2 Vec with TF-IDF-CDW, the word embeddings can better highlight key terms in the text, improving the category discrimination of feature words. In the feature learning stage, we have constructed a hybrid dual-channel architecture combining a GCNN and a two-layer BiLSTM, which realizes the complementary advantages of local features and contextual dependencies and assigns different weights to dual-channel semantic information using an attention mechanism. The effectiveness of our method was validated using two publicly available datasets. Experimental findings demonstrate that it consistently surpasses several baselines across both datasets. Specifically, for the THUCNews dataset, the model achieved 91.85% accuracy and an F1-score of 91.84%, indicating that the integration of enhanced word embeddings and a hybrid neural structure can significantly boost classification performance. For the TNews dataset, it obtained 87.70% accuracy and an F1-score of 87.72%, further confirming the model's robustness and adaptability across different textual domains. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Variation**} & \multicolumn{2}{c}{**THUCNews**} & \multicolumn{2}{c}{**TNews**} \\  & **Accuracy (\%)** & **Decreased (\%)** & **Accuracy (\%)** & **Decreased (\%)** \\ \hline Remove the TF-IDF-CDW & 91.56 & 0.29 & 87.29 & 0.41 \\ Remove the POS encoding & 91.71 & 0.14 & 87.52 & 0.18 \\ Only keep Word2 Vec & 91.52 & 0.33 & 87.20 & 0.50 \\ Remove the GCNN layer & 90.51 & 1.34 & 85.66 & 2.04 \\ Remove the BiLSTM layer & 90.82 & 1.03 & 86.22 & 1.58 \\ Our model & 91.85 & - & 87.70 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation experimental results. Although the proposed model performs well overall, the confusion matrices in the experimental results show higher misclassification rates for categories with substantial semantic overlap, such as finance and stocks. Future research may explore constructing a hierarchical classification framework to reduce misclassification between adjacent domains. Additionally, the number of samples in some specialized domains is limited. Future research could explore the model's performance in specialized fields by combining data augmentation strategies and adjusting the model to adapt to different domains. Conceptualization, Z.X. and C.L. ; methodology, Z.X. and C.L. ; software, Z.X. ; validation, Z.X. ; formal analysis, Z.X. and H.W. ; data curation, Z.X. and H.W. ; writing--original draft preparation, Z.X. ; writing--review and editing, Z.X. and C.L. ; visualization, Z.X. ; supervision, C.L. ; project administration, C.L. ; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This paper uses public datasets for experiments. THUCNews is available at [[https://github.com/thunlp/THUCTC](https://github.com/thunlp/THUCTC)]([https://github.com/thunlp/THUCTC](https://github.com/thunlp/THUCTC)) (accessed on 3 March 2025). TNews is available at [[https://github.com/aceimnostuvwxz/toutiao-text-classification-dataset](https://github.com/aceimnostuvwxz/toutiao-text-classification-dataset)]([https://github.com/aceimnostuvwxz/toutiao-text-classification-dataset](https://github.com/aceimnostuvwxz/toutiao-text-classification-dataset)) (accessed on 3 March 2025).