## 3 Research Design
### Research Method and Process
#### 3.2.2 Research Process  
The process of data acquisition and analysis is as follows (shown in Figure 1):Step 1, determining the search strategy. The CNKI literature database is selected as the data sources to ensure the thorough gathering of data. CNKI (China National Knowledge Infrastructure) is currently the most comprehensive academic repository in China, with a total of over 200 million titles, containing more than 95% officially published Chinese academic resources. Therefore, using the CNKI database as the data source ensures adequate access to high-quality Chinese research in this field, which fits our research aims. Most research on topic identification or prediction is found in the scientific literature. In addition, science and technology patents are also important entities reflecting the generation and application of domain knowledge topics. The search terms \"research frontiers\", \"dynamic evolution\", \"link prediction\" and \"network links\" have been excluded for the sake of search completeness and accuracy. After several trial searches, an advanced search has been conducted with the keywords \"technology identification\" OR \"technology prediction\" OR \"subversive technology\", excluding \"topic identification\" OR \"topic prediction\" OR \"emerging topics\" OR \"topic evolution\". The document types are limited to journal papers, conference papers and dissertations, with no time span. The search deadline is 30 June 2022.  
Step 2, screening the study sample. First, we read the titles, abstracts and keywords of the obtained literature to filter out the samples that are relevant to this field. Then, we review the full text of these samples one by one, selecting articles that explicitly identify the use of models and algorithms for topic analysis in their research methodology, finally choosing 96 document as the research sample of this paper.  
Step 3, analyzing models and algorithms used in the sample literature. First, the bibliographic data of 96 documents are imported into the tool for co-word analysis and mapping the co-word network. Second, we create an EXCEL document, and manually scan and extract the main models and algorithms used in the sample literature from two perspectives: topic identification and trend prediction. These methods are recorded according to the original text of the paper. Three experts read all samples separately and decide on the final version of the EXCEL document. Then, classification is conducted to calculate the frequency of models and algorithms applied in the sample literature. A consistency test is conducted by randomly selecting the records of three researchers and calculating the Cohen's Kappa statistic score. The Cohen's Kappa scores are here all above 0.8 (0.75 indicates good consistency), indicating credible results.  
Step 4, constructing an indicator system and related verification methods. We extract and summarize the indicators used in the documents to construct an indicator system for topic identification and prediction. The indicator validation methods used in the literature are grouped.  
Figure 1: Framework of research process.