## 2 Literature Review
However, semantic ambiguity in the patent text information analysis may affect the accuracy of the analysis results. [PERSON] et al. [15] designed and developed ESCAPE (enhanced self-tuning characterization of document collections after parameter evaluation). It integrated two different solutions for document clustering and topic modeling: the joint approach and the probabilistic approach. Both approaches are able to correctly identify meaningful partitions of a given document corpus by grouping them according to topics. Escape minimizes user intervention and the method works well for large texts. For small text, the parameters need to be chosen in the context of the target domain. Patents belong to short text data and TF-IDF applies to short text of patents. Therefore, in this paper, we use this method to set weights to remove some high-frequency but not technically meaningful words. [PERSON] et al. [12] conducted research on identifying the technology evolution path in the field of hydrogen energy, adopted text mining methods to mine patent data, and introduced document vectorization and phrase mining algorithms to improve the mining depth while increasing the interpretability of the results. [PERSON] et al. [28] constructed, through the unsupervised LDA topic clustering method and the main fields of activity, blank fields, and fields of agricultural machinery were visually analyzed. [PERSON] et al. [13] used the SAO structure extraction technique for the patent text to obtain the effective content of the text, and acquisition of core technologies in the field of mechanical structures. [PERSON] et al. [29] used SAO semantic mining methods to mine technical problems and solutions contained in scientific and technical papers and patents. [PERSON] et al. [14] mined the abstracts of wind energy technology patent documents and used the k-means clustering algorithm to determine the distribution of the keywords among the clusters. Unmanned ship technology presents unprecedented prospects, but existing research on the identification and evolution analysis of unmanned ship technology topics is limited. [PERSON] et al. [30] utilized the LDA topic model to extract technical themes and keywords from unmanned ship patent text. They constructed a co-occurrence network based on unmanned ship technology keywords to analyze the evolution process of technical topics and keywords, identifying technology opportunities. [PERSON] et al. [31] used BERT to classify keywords in unmanned ship patent texts and used TEMPEST to cluster technical topics. Next, the function-oriented search (FOS) method is applied to extract fresh technical elements from patents across various fields. This process involves reorganizing both original and novel technology elements to pinpoint technical opportunities. Previous studies have identified and analyzed unmanned ship technology from various perspectives, guiding this study in understanding the development direction of unmanned ship technology. However, the above research is lacking in terms of specific technical theme systems, identification of evolution rules, and future development trends in the field of unmanned ship technology. Deep learning models typically require large amounts of labeled data for training. It is more practical for large corpora or real-time processing tasks, which require large computational resources or long training times. However, LDA and TF-IDF are typically more computationally efficient and less resource-intensive than deep learning models. It achieves better performance on smaller datasets for lower data volumes and providesrelatively intuitive and interpretable results. The patent data of unmanned ships are small and the abstract text is short; this paper chooses LDA and TF-IDF for text topic mining. In conclusion, the current patent technology theme identification and evolution analysis research has achieved specific results.