[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81480-en":3,"doc-seo-81480-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81480,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Mining and Searching Association Relation of Scientific Papers Based on Deep Learning","Scientific papers contain complex correlation among their data, reflecting field-specific data characteristics, rules, and relationships that enable analysis of scientific and technological big data. Deep learning–based mining and searching of association relationships offers practical value for supporting scientific researchers. The approach addresses shortcomings of traditional keyword-matching search by learning feature semantics, extracting entities, and modeling deep associations to build a shared semantic space and enable efficient retrieval, ranking, and personalized relevance via feedback mechanisms.","Mining and Searching Association Relation of Scientific Papers  \nBased on Deep Learning  \nJie Song  \n[songs@bupt.edu.cn](songs@bupt.edu.cn)[ ](songs@bupt.edu.cn)School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications Beijing, China  \nMeiyu Liang∗ Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications Beijing, China  \nZhe Xue  \nBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications Beijing, China  \nFeifei Kou  \nBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications Beijing, China  \nAng Li  \nBeijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications Beijing, China  \narXiv :2204 . 1 1488v2 [ cs .DL] 10 Jul 2026  \nAbstract  \nThere is a complex correlation among the data of scientific papers. The phenomenon reveals the data characteristics, laws, and correlations contained in the data of scientific and technological papers in specific fields, which can realize the analysis of scientific and technological big data and help to design applications to serve scientific researchers. Therefore, the research on mining and searching the association relationship of scientific papers based on deep learning has far-reaching practical significance.  \nKeywords  \nscientific paper; analysis; search technology of scientific papers; mining technology of scientific papers  \nDifferent from the keyword matching method of the traditional search mode, the search of scientific papers is aimed at scientific researchers, which has efficient and accurate search requirements and needs to further explore the correlation relationship between scientific papers in the search results. In addition, the study relating to scientific papers faces many challenges, such as the accuracy of category segmentation, the effectiveness of entity extraction, and other problems. The traditional keyword matching search mode cannot effectively solve these problems. Feature semantic learning is carried out on the basis of existing scientific papers, so as to discover potential semantic correlations in scientific papers and establish a public semantic representation space of scientific papers from different sources. Based on interest ranking, relevance ranking, feedback mechanism, and ranking optimization mechanism, one can realize the retrieval and sorting of scientific papers and efficient partition indexing of inaccurate multi-channel information input. Furthermore, based on the deep association model, through the in-depth mining and analysis of the searched massive scientific and technological papers, the extraction and display of specialized, personalized, and orderly associations can be realized.  \n∗ Corresponding author.  \n1 Basic Knowledge  \nA pre-trained model is a pre-trained and saved network that was previously trained on a large dataset. The basic idea of this natural language representation is used in a series of applications ranging from Word2vec[1] to BERT[2] . Pre-trained language models are commonly used models recently. As a method of generating word vectors to pre-train neural networks, Word2vec is the first method to use pre-training natural language. The purpose of the attention mechanism is to make the model simulate the human attention mechanism to pay attention to important information. ELMo[3] generates dynamic word vector representations according to the context on this basis, and Transformer is a feature extractor that uses the self-attention mechanism. Retrieval-oriented maskedautoencoder pre-training, exemplified by RetroMAE, further specializes language representations for matching and retrieval[4] . Therefore, the performance of the language model has been greatly improved. BERT pre-","cbCaiuSfDDmP4xjN","https://ap.wps.com/l/cbCaiuSfDDmP4xjN","pdf",372050,4,1,7,"English","en",105,"# Abstract\n# Keywords\n# Basic Knowledge\n## Pre-trained language models\n## Attention mechanism and Transformers\n## Graph representation learning","[{\"question\":\"Why do scientific papers require association mining beyond keyword matching?\",\"answer\":\"Scientific paper data exhibit complex correlations across specific fields. Keyword matching cannot effectively handle issues such as category segmentation accuracy and entity extraction effectiveness.\"},{\"question\":\"What role does feature semantic learning play in the proposed method?\",\"answer\":\"Feature semantic learning discovers latent semantic correlations and builds a public semantic representation space for scientific papers from different sources.\"},{\"question\":\"How does the approach use deep learning to improve retrieval and ranking?\",\"answer\":\"It relies on deep association models plus interest/relevance ranking and feedback-driven ranking optimization to extract and present specialized, personalized, and ordered associations while supporting efficient indexing of multi-channel inputs.\"}]",1784173732,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mining-and-searching-association-relation-of-scientific-papers-based-on-deep-learning","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/mining-and-searching-association-relation-of-scientific-papers-based-on-deep-learning/81480/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do scientific papers require association mining beyond keyword matching?","Question",{"text":75,"@type":76},"Scientific paper data exhibit complex correlations across specific fields. Keyword matching cannot effectively handle issues such as category segmentation accuracy and entity extraction effectiveness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does feature semantic learning play in the proposed method?",{"text":80,"@type":76},"Feature semantic learning discovers latent semantic correlations and builds a public semantic representation space for scientific papers from different sources.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the approach use deep learning to improve retrieval and ranking?",{"text":84,"@type":76},"It relies on deep association models plus interest/relevance ranking and feedback-driven ranking optimization to extract and present specialized, personalized, and ordered associations while supporting efficient indexing of multi-channel inputs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]