[Objective] This paper tries to construct data analysis model for the topics of scientific research based on machine learning. [Methods] First, we clustered data with the Latent Dirichlet Allocation model. Then, we investigated the correlation among year, institution and research types with the help of Python modules. Finally, we revealed and visualized the key research areas of every year or institution. [Results] We analyzed 101,813 papers and patents of graphene industray research. The proposed method finished the topic identification, correlation analysis, and visualization in about two miniutes. [Limitations] More research is needed to explore the network analysis issues. [Conclusions] Machine learning provides enormous potentiality for intelligence studies, especially the large volume text analytics and visualization.
王丽,邹丽雪,刘细文. 基于LDA主题模型的文献关联分析及可视化研究[J]. 数据分析与知识发现, 2018, 2(3): 98-106.
Li Wang,Lixue Zou,Xiwen Liu. Visualizing Document Correlation Based on LDA Model. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2017.1058.