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| Identification of Potential Emerging Technologies by Fusing SVM-LDA and Weighted Similarity: Taking the Field of Artificial Intelligence as an Example |
| Ran Congjing, Tian Wenfang |
| School of Information Management, Wuhan University, Wuhan 430072 |
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Abstract In the context of a new round of technological revolution and accelerated industrial transformation, accurately identifying emerging technologies with disruptive potential in the constantly emerging technological ocean is of great significance for the nation, enterprise participants, and relevant commercial investment institutions. It is therefore important to grasp the development trends and directions of technological innovation, reasonably allocate scientific and technological resources, and carry out advance scientific and technological strategic planning and technological layout. This article proposes an emerging technology topic recognition model based on knowledge-enhanced SVM-LDA. First, a classification standard for basic technology was developed based on the prior knowledge of the expert group; second, the technology category classification criteria were input into the SVM-LDA model as a priori knowledge to obtain the technology topic clustering results; third, a weighted similarity calculation based on category subject terms was performed to identify potential emerging key technologies; and fourth, an empirical study was conducted using the field of artificial intelligence as an example. Finally, 24 potential emerging technologies were obtained, mainly distributed across six major categories: special robot technology, monitoring and early warning technology, video and image processing technology, voice recognition technology, automated planning and decision-making techniques, and natural language processing technology.
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Received: 07 October 2023
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