# Research Frontiers in the Field of Agricultural Resources and the Environment
## 4 Discussion
### About the Limitations and Difficulties  
However, there are still some shortcomings related to the methods of topic extraction, discriminant indicators, and data source selection. The inherent limitations and difficulties in this study include the available time selected, differences in geographic space, the insufficient number of texts that make up the corpus, and limitations to the technology used. These factors may lead to limitations and inaccuracies in the research results, which need to be overcome and improved in subsequent studies.  
In the process of applying topic extraction models, problems such as low data quality, and the overlapping or blurring of topics may be encountered. To overcome these difficulties, we took a series of measures, such as modifying the data extraction methods, optimizing data processing, improving model parameter settings, and introducing more external knowledge, etc., to improve the accuracy and reliability of the topic extraction. In this study, the number of topics determined was the first and most important step, and it mainly related to the number of iterations. We continuously adjusted the two above parameters and used perplexity algorithms and visualization conducted by pyLDAvis to evaluate the training results, until the optimal number of topics was determined. In addition, we also screened for keywords in papers in the field of agricultural resources and the environment and added them to the corpus to enrich its content, in order to achieve better thematic effects. At the same time, relevant experiences and research results from other fields were also borrowed to provide more ideas and methods for overcoming these difficulties.  
In this study, topic extraction was based on the LDA model. Although the topic extracted by the LDA algorithm largely compensated for the shortcomings of citation analysis and vocabulary analysis methods, the current topic recognition results still have more single words and fewer keywords in the form of phrases, and the expression of the topic content was not rich enough. In addition, the current data sources only involve paper data and project data, which represent the theme of basic research, but do not consider and integrate data sources such as planning texts, policy texts, and patents representing technology research and development.  
In the future, it is necessary to increase the types of data sources and conduct extensive and in-depth analysis. Combining the tools and methods with the semantic analysis function of the text content, it should strengthen the preprocessing of datasets, improve the custom dictionary and the accuracy of the subject phrase recognition, in order to put forward more precise frontiers. The selection of indicators for each dimension still needs to be further optimized, and the weight of the indicators needs to be scientifically improved in combination with the characteristics and contributions of data sources. By increasing the multidimensional data sources, optimizing the topic extraction models, and improving the judgment indicators, comprehensive support can be provided for scientific research and management layout decisions.