# Research on feature fusion and multimodal patent text based on graph attention network

arXiv: https://arxiv.org/abs/2505.20188 · HTML: https://mlsanity.com/paper/2505.20188/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 1 of 1)

- [Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method](https://mlsanity.com/paper/2401.06782/) (2024)

## Built on by (most cited first, 5 of 5)

- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems](https://mlsanity.com/paper/2506.17551/) (2025)
- [Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs](https://mlsanity.com/paper/2507.16672/) (2025)
- [Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems](https://mlsanity.com/paper/2506.11421/) (2025)
- [LLM-Augmented Symptom Analysis for Cardiovascular Disease Risk Prediction: A Clinical NLP](https://mlsanity.com/paper/2507.11052/) (2025)

## Abstract (arXiv)

Aiming at the problems of cross-modal feature fusion, low efficiency of long text modeling and lack of hierarchical semantic coherence in patent text semantic mining, this study proposes HGM-Net, a deep learning framework that integrates Hierarchical Comparative Learning (HCL), Multi-modal Graph Attention Network (M-GAT) and Multi-Granularity Sparse Attention (MSA), which builds a dynamic mask, contrast and cross-structural similarity constraints on the word, sentence and paragraph hierarchies through HCL. Contrast and cross-structural similarity constraints are constructed at the word and paragraph levels by HCL to strengthen the local semantic and global thematic consistency of patent text; M-GAT models patent classification codes, citation relations and text semantics as heterogeneous graph structures, and achieves dynamic fusion of multi-source features by cross-modal gated attention; MSA adopts a hierarchical sparsity strategy to optimize the computational efficiency of long text modeling at word, phrase, sentence and paragraph granularity. Experiments show that the framework demonstrates significant advantages over existing deep learning methods in tasks such as patent classification and similarity matching, and provides a solution with both theoretical innovation and practical value for solving the problems of patent examination efficiency improvement and technology relevance mining.