## 2 Literature Review  
Identifying patented technologies and analyzing their evolution is crucial for understanding technological trends and fostering innovation. Table 1 provides an overview of the state of the art in topic modeling and recognition methods. The academic community has extensively researched these topics, focusing on three main analysis methods: patent classification numbers, patent citation networks, and patent text content. The research objectives of this paper are to review the development of unmanned ship technology and describe the current path of growth in the field. By constructing a framework for mining technologies, the readers can better understand the detailed technologies under each category. The proposed methodological framework can be used further to explore other technological aspects of unmanned ship technology. \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 classification \\ numbers \\ \end{tabular} } & \multirow{2}{*}{[PERSON] et al. [6]} & LDA, text similarity calculation & \multirow{2}{*}{blockchain field patents} & Technological evolution & \multirow{2}{*}{Inaccurate, Without objectivity} \\ \cline{2-2} \cline{6-6}  & & & & & & \\ \cline{2-2} \cline{6-6}  & & & & & & \\ \cline{2-2} \cline{6-6}  & & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Patent citation \\ networks \\ \end{tabular} } & \multirow{2}{*}{[PERSON] et al. [7]} & \multirow{2}{*}{IPC co-category analysis} & \multirow{2}{*}{hydrogen fuel cell patents} & \multirow{2}{*}{map the knowledge map of hotspots} & \multirow{2}{*}{\begin{tabular}{c} Inaccurate, \\ Insufficient \\ information \\ \end{tabular} } \\ \cline{1-1} \cline{3-5}  & & & & & & \\ \cline{1-1} \cline{3-5}  & & & & & \\ \cline{1-1} \cline{3-5}  & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Patent citation \\ networks \\ \end{tabular} } & \multirow{4}{*}{[PERSON] et al. [9]} & \multirow{4}{*}{[PERSON] et al. [9]} & \multirow{4}{*}{[PERSON] et al. [9]} & \multirow{4}{*}{Graphene patents} & \multirow{4}{*}{\
\begin{tabular}{c} identifying technology \\ development trends \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} maybe errors in \\ explaining the \\ relationship \\ \end{tabular} } \\ \cline{1-1} \cline{3-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of related works. The method based on patent classification numbers aims to grasp technical connections and evolutionary relationships across various fields by analyzing patent classification numbers. This helps uncover related technologies' development history and trends in different domains [16; 17]. For example, [PERSON] et al. [6] used LDA to conduct technical evaluation and roadmap analysis in the blockchain field based on patent data and predicted future development trends. [PERSON] et al. [7] used IPC joint analysis to map the hotspot knowledge map in the hydrogen fuel cell technology field and then identified hotspot technologies. [PERSON] et al. [8] further analyzed the development trend of technology in electric vehicle charging by analyzing high-frequency IPC classification numbers and their time distribution. The analysis method based on patent classification numbers can analyze the technological evolution process to a certain extent [18]. However, classification based on patent classification numbers may be inaccurate or subjective. In addition, the analysis method does not go deep into the patent text content. It cannot cover the specific details and characteristics of the technology, which may affect the accurate understanding of technological evolution. The patent citation network analysis method constructs networks between technical fields using citation relationships among patent documents. This approach calculates network attributes to unveil potential technical themes [19; 20]. For example, [PERSON] et al.