MFT-PTM: A Multisource-Fused and Temporally-Aware Framework for Evolutionary Analysis of Rare Earth Patent Topics Model

Rare-earth elements are critical to a wide range of high-technology applications, and analyzing patents involving rare-earth elements is essential for understanding technological progress and innovation trends. Traditional topic models cannot fully exploit patent network structures and temporal information, limiting their ability to capture the dynamic evolution of technology topics. To overcome these limitations, we propose a novel multisource-fused framework (MFT-PTM), which integrates three types of multisource features: textual, network, and temporal features via the time-aware TemporalK-Means algorithm. Specifically, we use SciBERT to extract text embeddings, TransR to generate network embeddings, and derive temporal scalars from patent data. After fusing and reducing these features with Uniform Manifold Approximation and Projection (UMAP), we apply TemporalK-Means clustering with a time-decay mechanism to capture evolutionary trends. Experiments on 43,322 rare-earth-related patents indicate that the proposed framework achieves improved performance compared with traditional methods such as LDA and BERTopic in terms of topic coherence, cluster quality, and cluster separation. Furthermore, the analysis suggests a noticeable technological transition in rare-earth applications, gradually shifting from environmental catalysis toward advanced energy and biomedical domains. Overall, the framework provides a quantitative approach for integrating multisource patent information and exploring technological evolution patterns.