Topic Analysis And Recommendation System Based On Scientific Research Documents

Scientific research document data contains a wealth of scientific research information,such as research content,research methods and so on.With the increasing scale of scientific research documents,it is increasingly difficult for researchers to obtain valuable information from a large number of scientific research documents.It is urgent to accurately extract and effectively organize the useful information in scientific research documents.After research,analyzing the research documents from the dimension of topic and mining the trend of the topic will help researchers to obtain research trends in relevant fields in a timely manner,and more convenient and efficient use of scientific research resources.This paper chooses the classic Latent Dirichlet Allocation(LDA)model for topic mining.However,the topic excavated by LDA model is presented in the form of word probability distribution,which is relatively abstract.In order to make the mined topics easier to understand,we extract a few sentences from the document set that are most similar to the meaning of topic as topic label,and we make the content of the topic labels more refined by reducing the repetition between sentences.In addition,this paper designs a recommendation function for similar topics to measure the similarity of topics by calculating the cosine similarity between the distribution of topic words.Finally,the paper also designed the trend analysis function of the topic heat,which measures the heat of the topic by calculating the number of documents related to the topic in each year,and uses the trend curve to express the trend of the topic heat with the year.In view of the above requirements,the paper has carried out reasonable module design and architecture design,and implemented a topic analysis and recommendation system based on scientific research documents,and more comprehensively excavated valuable information in scientific research documents from the perspective of the topic.It provides a new perspective for researchers to analyze scientific research documents,improves the efficiency of analyzing documents,and has good research and application value.