## 7 Indicator System and Effectiveness Verification
### Indicator System  
In the identification and prediction of topics, the scientific and reasonable construction of relevant indicator systems and feature selection is a key process. Based on a literature analysis, a more systematic and comprehensive indicator system has been built with the following five categories: basic scalar, topic popularity and intensity, emerging degree, cross diffusion and network structure, as shown in Table 2. The table also lists the specifics of each category (see Secondary Category column), the corresponding indicator of the measurement used and the reference source. Due to the large volume of master's and doctoral dissertations and the broad coverage of system models and research methods, this paper mainly summarizes the characteristics of the indicators involved in 67 journal papers, excluding dissertations. Among these, the basic scalar feature indicators are generally obtained through more direct descriptive quantitative statistics. Time series prediction is conducted based on word frequency or citation frequency. For example, [PERSON] et al. used word frequency time series integration to predict popular topic words for a period of time in the future through subject word mining [80]. [PERSON] et al. conducted time series evolution analyses using the quantitative trend evolution model of technology prediction to predict new technology and development trends in the area of volatile organic compounds [68]. [PERSON] et al. pointed out that LSTM and other time series prediction models can be applied to topic correlation and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Secondary Category** & **Indicator** & **References** \\ \hline \multirow{3}{*}{Basic scalar} & Word frequency & Number of subject words in documents or patents & [PERSON] et al., (2020) [80] \\  & Citation/Cited quantity & Citation frequency of documents or patents & [PERSON] et al., (2020) [68] \\  & Scale & Number of patented products/patentees & [PERSON] et al., (2020) [36] \\ \hline \multirow{3}{*}{\begin{tabular}{c} Topic \\ popularity \\ and intensity \\ \end{tabular} } & Topic popularity & Weighting of the number of documents issued and & [PERSON] et al., (2019) [37] \\  & Topic intensity & Number or proportion of supporting documents & [PERSON] et al., (2021) [76] \\  & & Keyword weight & [PERSON] et al., (2021) [9] \\ \hline \multirow{3}{*}{\begin{tabular}{c} Emerging \\ degree \\ \end{tabular} } & Topic novelty & Article/patent publication age & [PERSON] et al., (2020) [85] \\  & Topic growth rate & Slope of curves of document/patent authorization & [PERSON] et al., (2022) [58] \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Cross diffusion \\ \end{tabular} } & Crossing degree & Distribution of subject words and their cross & [PERSON] et al., (2018) [86] \\  & Scientific relevance & Number of scientific documents cited by patents & [PERSON] et al., (2021) [87] \\  & Topic relevance & The correlation of topic adjacent time evolution & [PERSON] et al., (2020) [36] \\  & Migration degree & Transition probability of the topic to the next time & [PERSON] et al., (2019) [47] \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Network \\ structure \\ \end{tabular} } & Topic node centrality & Degree/intermediary/proximity/eigenvector centrality & [PERSON] et al., (2019) [47] \\  & Local information & Common Neighbor/Cosine & \\  & similarity & Similarity/Jaccard/Sorenson/ & [PERSON] et al., (2021) [74] \\  & & Priority Link, etc. & \\ \cline{1-1}  & Path similarity & Local path/Katz & \\ \cline{1-1}  & Random walk & Average commuting time/cosine similarity based on & [PERSON] et al., (2019) [83] \\ \cline{1-1}  & similarity & random walk/SimRank & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Topic identification and prediction indicator system. prediction under multiple time series windows [36]. The characteristic index used in basic scalar is the change in or patent subject words and word frequency in the literature. Topic popularity is also called theme intensity in some studies.