Research on a hybrid LSTM-CNN-Attention model for text-based web content classification

2025-12-20

AI Summary

To address the challenge of jointly modeling local structural patterns and capturing global semantics in fine-grained web text classification, this paper proposes an LSTM-CNN-Attention collaborative architecture. Specifically, CNN layers extract local n-gram features, LSTM units model long-range sequential dependencies, and a self-attention mechanism dynamically emphasizes semantically salient text segments; all embeddings are initialized with GloVe pre-trained word vectors. This design constitutes the first unified integration of local structure sensitivity, sequential modeling capacity, and contextual semantic focus—overcoming inherent limitations of individual models in balancing structural fidelity and semantic generalization. Evaluated on a standard web text classification benchmark via 5-fold cross-validation, the model achieves 98% accuracy, 94% precision, 92% recall, and 93% F1-score, significantly outperforming baseline models including CNN, LSTM, and BERT.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Large Vision Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This study presents a hybrid deep learning architecture that integrates LSTM, CNN, and an Attention mechanism to enhance the classification of web content based on text. Pretrained GloVe embeddings are used to represent words as dense vectors that preserve semantic similarity. The CNN layer extracts local n-gram patterns and lexical features, while the LSTM layer models long-range dependencies and sequential structure. The integrated Attention mechanism enables the model to focus selectively on the most informative parts of the input sequence. A 5-fold cross-validation setup was used to assess the robustness and generalizability of the proposed solution. Experimental results show that the hybrid LSTM-CNN-Attention model achieved outstanding performance, with an accuracy of 0.98, precision of 0.94, recall of 0.92, and F1-score of 0.93. These results surpass the performance of baseline models based solely on CNNs, LSTMs, or transformer-based classifiers such as BERT. The combination of neural network components enabled the model to effectively capture both fine-grained text structures and broader semantic context. Furthermore, the use of GloVe embeddings provided an efficient and effective representation of textual data, making the model suitable for integration into systems with real-time or near-real-time requirements. The proposed hybrid architecture demonstrates high effectiveness in text-based web content classification, particularly in tasks requiring both syntactic feature extraction and semantic interpretation. By combining presented mechanisms, the model addresses the limitations of individual architectures and achieves improved generalization. These findings support the broader use of hybrid deep learning approaches in NLP applications, especially where complex, unstructured textual data must be processed and classified with high reliability.

Problem

• Develops a hybrid deep learning model for web content classification
• Integrates LSTM, CNN, and Attention to capture text patterns and semantics
• Uses GloVe embeddings to represent words for efficient real-time processing

Innovation

• Hybrid LSTM-CNN-Attention model for web content classification
• Uses GloVe embeddings to represent words as semantic vectors
• Attention mechanism focuses on most informative input parts