## 5 Discussion  
This study uses the LDA topic model and TF-IDF text vectorization method to mine and identify unmanned ship patent technology topics, and analyze the intensity evolution analysis and content evolution of technical topics in unmanned ship technology. Unmanned ship technology has a large development space in the fields of perception technology, navigation technology, control technology, and power technology. TF-IDF and LDA are two commonly used methods in text processing, and they each have different characteristics and applicable scenarios. In some cases, combining TF-IDF with LDA may bring better results, while in other cases, it may not be necessary or appropriate to use them together. 1. Against the use of TF-IDF with LDA:  
When computational resources are limited, it may not be necessary to use TF-IDF and LDA at the same time. Both methods require a certain amount of computational resources, especially when dealing with large amounts of text data. If resources are tight, one of the methods can be chosen according to specific needs. For some simple text processing tasks, such as simple keyword search or basic text categorization, it may be sufficient to use only one of the TF-IDF or LDA methods. In such cases, using a combination of both methods may add unnecessary complexity. 1. In favor of using TF-IDF with LDA:  
In some cases, combining TF-IDF with LDA may give better results. When we need to extract topics and identify keywords from the text at the same time, we can use TF-IDF in combination with LDA. TF-IDF can help identify keywords in the text, while LDA can extract potential topics from the text. In this way, we can obtain the topic distribution and keyword distribution of the text at the same time, so as to understand the text content more comprehensively. TF-IDF and LDA can also be used in combination with the task of text classification or clustering. TF-IDF can be used for feature extraction to convert text into numeric vectors, while LDA can be used to further extract the underlying thematic structure of the text on this basis. This helps to improve the accuracy of classification or clustering. In text summarization or recommender systems, the combined use of TF-IDF and LDA can help extract key information and recommend content on relevant topics.TF-IDF identifies  
Figure 9: Technology roadmap. keywords and phrases in the text, while LDA makes content recommendations based on potential topics. In this paper, we obtain 35 unmanned boat technology topics. Two hot technologies, six growing technologies, and six declining technologies are obtained through theme evolution. By analyzing the changes in technologies between time periods, this paper suggests future technology development for unmanned ships. In the field of perception technology, there is a need to strengthen the development and optimization of perception technology for unmanned ships and focus on exploring potential technologies related to water quality/water area detection, surveying and mapping technology, target detection, and garbage cleanup. In the research and development and application of unmanned ship sensing technology, it is necessary to actively research and develop various sensors and sensing algorithms to accurately obtain environmental information around unmanned ship technology, including water quality, waters, terrain, water surface objects, etc. This sensing information provides a decision-making basis for the autonomous navigation of unmanned vessels and data support for monitoring and managing the water environment. At the same time, it is necessary to optimize the target detection algorithm further in computer vision. These algorithms can detect objects, obstacles, fairways, etc., that surround the unmanned ship in real time, helping the unmanned ship to navigate autonomously and avoid obstacles. The current focus of research is to use sensing technology to use sensors, cameras, and other equipment to identify, locate, and clean up garbage and other obstacles on the water surface.