[Purpose/significance] Analyzing the evolution of technological topic makes it possible for us to track the development of technology, which is essential for improving innovation activity and forecasting development trends of technology. However, to our knowledge, scholars pay less attention to the semantic perspective of technological topic. Therefore, this paper intends to analyze the evolution of technological topic from the perspective of semantic.[Method/process] This paper proposed a dynamic topic model based on non-negative matrix factorization, and labeled the technology topics with noun phrases extracted by TextRank algorithm, which enhances the interpretability. Then, the study computed and visualized the evolutionary trajectory of technological topics with word embedding.[Result/conclusion] This paper uses five countries' (China, America, Japan, South Korea, Europe) patent data in 2002, 2005, 2008, 2011 and 2014 to test our model. During the course of the experiment,our method extracted evolutionary trajectories of 65 technical topics, which verified the effectiveness of our method.
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