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| Technology Prediction Method Based on Data Fusion and Life Cycle: Empirical Analysis of Virus Nucleic Acid Detection |
| Zhang Yang, Lin Yuhang, Hou Jianhua |
| School of Information Management, Sun Yat-sen University, Guangzhou 510006 |
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Abstract The construction of an efficient prediction model depends on the selection of the appropriate basic data. Given the characteristics of virus nucleic acid detection technology, and the shortcomings of existing research, such as considering a single data source and ignoring the impact of technology life cycle, this paper proposes an improved technology prediction method based on link prediction algorithm. According to the basic data set selected by considering the technology life cycle theory, we establish a weighted co-occurrence network of technical-subject-fields. This is achieved by integrating patents and documents as data sources. The empirical research and comparative analysis is conducted through a multi-level and multi-stage prediction method. Experimental results show that under the weighted link prediction index, compared with a single data source, the prediction effect of the fusion data is significantly improved. Finally, combined with the law of technology evolution and the theory of life cycle, this study provides a valuable reference for delimiting the time range of data base, which can further raise the prediction efficiency and accuracy.
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Received: 23 March 2020
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