## 2 Literature Review
[21] used the difference function to identify emerging technology topics based on direct citations and co-citations. [PERSON] et al. [9] constructed an SAO network and identified graphene technology development trends by analyzing structural holes, node degree distribution, and network centrality changes. [PERSON] et al. [22] used point degree centrality and structural holes to identify and compare emerging technology fields across the Taiwan Strait based on patent citation network analysis. [PERSON] et al. [23] constructed a mobile phone chip technology patent citation network based on topological structure parameters to identify core technologies. They used the SPNP algorithm to analyze the main path of technology diffusion. The patent citation network analysis method is an effective technology identification and analysis tool [24; 25]. However, this method relies on citation relationships between patent documents, making it difficult to identify specific technical subjects and keywords and ignoring the diversity of technological evolution paths. Moreover, this analysis method pays close attention to highly cited patent documents and easily ignores low-cited or emerging technology patent documents. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Paper** & **Model Used** & **Real Dataset** & **Special Features** & **Limitations** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Patent \\ Text content \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Maps et al. [10] \\ \end{tabular} } & \multirow{2}{*}{LDA} & Communication & \begin{tabular}{c} Increased institutional \\ topic probability \\ hierarchy \\ \end{tabular} & Without dynamic \\  & & & & & \\ \cline{2-5}  & \multirow{4}{*}{[PERSON] et al. [11]} & keyword network & 3D Printing, Big & \multirow{4}{*}{Technical forecasts} & \multirow{4}{*}{Inaccurate} \\  & & \begin{tabular}{c} analysis, \\ visualization \\ approach \\ \end{tabular} & \begin{tabular}{c} Data, Bitcoin, \\ Cloud \\ Technologies, \\ \end{tabular} & Technical forecasts & Inaccurate \\ \cline{2-5}  & \multirow{2}{*}{[PERSON] et al. [12]} & technology & hydrogen energy & \multirow{2}{*}{visualization} & Uncovered \\  & & evolution path & patent & & potential \\ \cline{2-5}  & \multirow{2}{*}{[PERSON] et al. [13]} & SAO, text & mechanical & \multirow{2}{*}{fast, accurate} & Without dynamic \\  & & similarity & structures patent & & characteristics \\ \hline [PERSON] et al. [14] & k-means, text mine & \begin{tabular}{c} Renewable energy \\ patent \\ \end{tabular} & data-driven analysis & \begin{tabular}{c} Unclear \\ evolutionary path \\ \end{tabular} \\ \hline \multirow{2}{*}{[PERSON] et al. [15]} & topic modeling, & scientific papers & \
\begin{tabular}{c} Automated \\ determination of \\ parameters \\ \end{tabular} &
\begin{tabular}{c} take a long time, \\ long text \\ \end{tabular} \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The analysis method based on patent text content is a method that uses the content of patent documents to identify and classify technical topics. The distribution and evolution of different technical topics are revealed by analyzing keywords, technical terms, invention abstracts, and other information in patent documents [26]. For example, [PERSON] et al. [10] used the LDA topic model to identify communication technology topics from patent titles and abstracts. They incorporated an institution-topic probability hierarchy to ascertain the distribution probabilities of competing enterprises within each topic and their technological standing. [PERSON] et al. [11] used the LDA topic model and Word2 vec vector model to identify core patent technology topics in artificial intelligence and analyzed the technical topic intensity and topic content evolution. Analyzing patent text content bypasses subjective judgment, enabling a comprehensive grasp of topic distribution and evolution in the technical realm. It facilitates timely detection and tracking of the latest technological advancements and trends [27].