[Purpose/Significance] To clarify the specific mechanisms of scientific knowledge flow to the technological domain and explore the key factors influencing this flow, can provide theoretical support for improving the efficiency of technology transfer. [Method/Process] First, based on the theory of knowledge flow, this paper constructed an influencing factor system with 9 categories and 18 indicators across three dimensions: knowledge actors, knowledge objects, and the environment. Then, using papers published in the field of biochemistry and molecular biology from 2011 to 2020 as a sample, a knowledge flow model was constructed through real and virtual non-patent citation relationships. Finally, the XGBoost machine learning model and SHAP interpretable machine learning method were applied to quantitatively analyze the influence mechanism of knowledge flow in non-patent citations. [Result/Conclusion] The study finds that knowledge relevance and paper authority are the core driving factors of knowledge transfer. In terms of knowledge subjects, journal authority and author authority have a significant impact on the transfer of scientific knowledge to technology. In terms of knowledge objects, highly relevant content and authoritative knowledge are more likely to facilitate knowledge transfer. This study identifies five major impact patterns: logarithmic, exponential, U-shaped, linear, and other types, revealing the dynamic effects of different features in the knowledge transfer process.
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