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| Research on Disruptive Technology Identification and Prediction Based on Deep Semantic Information Mining of Patent Texts |
| Wu Lei, Zhou Shufa, Lin Chaoran |
| School of Economics and Management, Harbin Engineering University, Harbin 150001 |
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Abstract This study addresses the limitations of existing methods in quantifying dynamic topic strength within deep semantic analysis, to enhance the accuracy and prediction of disruptive technology identification. We propose a multi-dimensional, dynamic model that couples bidirectional encoder representations from transformers topic (BERTopic) with dynamic topic modeling (DTM) and introduces “topic strength” to capture the emergence of technology topics. The model leverages the powerful semantic representation of the BERT language model to analyze inter-topic relationships and quantify their novelty using an adaptive thresholding algorithm based on Otsu’s method and quantiles. An empirical analysis of patent data from the integrated circuit (IC) industry demonstrates that our model identifies nascent and potentially disruptive technologies with greater sensitivity than traditional methods. Moreover, the integrated attention-based long short-term memory (LSTM) prediction module has a distinct advantage in terms of forecasting accuracy. This study offers a new paradigm for intelligence analysis of disruptive technologies by integrating semantic depth with a dynamic evolutionary perspective, thereby holding significant theoretical and practical value.
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Received: 31 March 2025
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