MA Chunjian, Wang Chao, XU Haiyun, WANG Lekang, ZHANG Xin, CHEN Liang
Current approaches to assessing the innovativeness of scientific and technological papers rely predominantly on expert peer review, a process that is often inefficient and subject to bias. Quantitative metrics offer greater objectivity but are largely retrospective and provide limited foresight or explanatory insight. This study proposes a novel framework for evaluating the innovativeness of stem cell scientific and technological papers using the DeepSeek model. Focusing on a corpus of stem cell research articles, the titles and abstracts of representative papers were vectorized using the bge-large-en-v1.5 model to construct a semantic vector database. Subsequently, the deepseek-reasoner model was applied to extract innovation-related features, which were organized into a vectorized innovation feature database. The two databases were subsequently integrated using a weighted fusion strategy. Target papers were then evaluated through FAISS-based vector retrieval and Top-k similarity matching within the unified database, resulting in a final innovativeness score and ranking. The results were rigorously validated against scores generated by the unassisted DeepSeek model to assess the framework’s effectiveness in evaluating innovativeness in biomedical scientific and technological papers. Empirical results indicate that the DeepSeek model tends to overestimate innovation when used without calibration. However, after targeted training, the model exhibits substantially improved stability and validity in innovation assessment, highlighting its strong potential for identifying innovative dimensions and distinguishing features in scientific literature.