[Purpose/significance] Based on semantic information, identifying the technological evolution trajectory in a hierarchical and gradual manner is helpful to strengthen the understanding of technical details and improve the accuracy of trajectory recognition.[Method/process] Firstly, this paper extracted the SAO structure of patents and scientific papers, determined the research topic based on semantic information, and used the S curve to determine the technology life cycle. Secondly, with the help of the machine learning algorithms and social network analysis indicators, the technology evolution trajectory was extracted and filtered in different cycles and at multiple levels. Taking the field of hematopoietic stem cells as the object of empirical analysis, this study found that there was a significant difference between the research focus of patents and scientific papers related to the subject of genetic etiology in this field. The topic had not yet developed a unified evolutionary path, and the researches on immune system diseases and diabetes were a potential evolutionary trend in the future.[Result/conclusion] The method proposed in this paper gradually realizes the extraction and condensing of complex technological evolution path through objective numerical calculation results. While revealing the main technological development process, method proposed in this paper can objectively predict the technological evolution trend.
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