[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85381-en":3,"doc-seo-85381-105":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},85381,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Research on Cross-media Science and Technology Information Data Retrieval","Since the big data era began, the Internet has become saturated with information, and everyday browsing depends on effective retrieval. Cross-media science and technology information differs from news and social data because it aggregates multi-source, multi-modal content with many semantically similar items and real-time technical hotspots. Traditional systems rely on unimodal keyword matching and outdated models, failing to satisfy scholars’ daily needs. This study supports deep semantic feature–based retrieval to align with current domestic and international technology-development trends.","Research on Cross-media Science and Technology Information  \nData Retrieval  \nYang Jiang  \nSchool of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications; Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing, China  \nZhe Xue∗ School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications; Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing, China  \nAng Li  \nSchool of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications; Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing, China  \narXiv :2204 .04887v 3 [ cs .IR] 11 Jul 2026  \nAbstract  \nSince the era of big data, the Internet has been flooded with all kinds of information. Browsing information through the Internet has become an integral part of people’s daily life. Unlike news data and social data on the Internet, cross-media science and technology information data has different characteristics. This data has become an important basis for researchers and scholars to track current hot spots and explore future directions of technology development. As the volume of science and technology information data becomes richer, traditional science and technology information retrieval systems, which support only unimodal data retrieval and use outdated keyword-matching models, can no longer meet the daily retrieval needs of science and technology scholars. Therefore, in view of this research background, it is of profound practical significance to study cross-media science and technology information data retrieval systems based on deep semantic features, in line with domestic and international technology-development trends.  \nKeywords  \ntechnology information, cross media, semantic learning, retrieval and query  \n1 Introduction  \nSince the era of big data, increasingly rich data has flooded all aspects of life. The various data types presented on the Internet can be used to meet the needs of different users, and the Internet has entered people’s lives and become an inseparable part of daily life. Unlike news and social information on the Internet [1], cross-media science and technology information data has a different character. For scholar-centered scientific resources, multi-view scholar clustering with dynamic interest tracking can represent both multiple research perspectives and evolving interests [2] . This data contains large amounts of rich information, reflects real-time information hotspots, and includes considerable semantically similar information. However, because it is multi-source and multi-modal, designing a unified process for collecting, filtering, storing, and processing it, and then forming related business applications or products, is necessary to meet market demand.  \n∗ Corresponding author.  \nAs science and technology information data becomes increasingly abundant, traditional retrieval systems for scientific scholars [3] have gradually lagged behind because they support only keyword matching. They can no longer meet scholars’ daily retrieval needs. Allowing researchers to retrieve more interesting and useful results from an ever-expanding volume places greater demands on retrieval systems. Interpretable machine-learning models can make the resulting intelligent decisions more transparent to users and system managers [4] . At the same time, unimodal retrieval will gradually give way to inter-modal retrieval [5]. Deeply integrating machine-learning and deep-learning algorithms with the characteristics of cross-media science and technology information data, and studying accurate cross-modal semantic-learning algorithms that support mutual retrieval, are therefore in line with domestic and international technology-development trends.  \n2 Acquisition and Feature Extraction Analy","cbCaitWPaVIL28RZ","https://ap.wps.com/l/cbCaitWPaVIL28RZ","pdf",385703,2,1,5,"English","en",105,"# Introduction\n# Acquisition and Feature Extraction Analysis of Cross-media S&T Information Data","[{\"question\":\"What makes cross-media science and technology information retrieval different from news or social data retrieval?\",\"answer\":\"Cross-media science and technology information is multi-source and multi-modal, containing many semantically similar items and real-time technical hotspots, unlike the characteristics of news and social data.\"},{\"question\":\"Why do traditional science and technology information retrieval systems no longer meet scholars’ daily needs?\",\"answer\":\"They mainly support unimodal keyword matching and use outdated keyword-matching models, which cannot retrieve sufficiently interesting and useful results as data volume increases.\"},{\"question\":\"What approach does the research propose to improve retrieval quality?\",\"answer\":\"It studies cross-media science and technology information retrieval systems based on deep semantic features, integrating machine-learning and deep-learning methods for cross-modal semantic learning and mutual 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makes cross-media science and technology information retrieval different from news or social data retrieval?","Question",{"text":75,"@type":76},"Cross-media science and technology information is multi-source and multi-modal, containing many semantically similar items and real-time technical hotspots, unlike the characteristics of news and social data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do traditional science and technology information retrieval systems no longer meet scholars’ daily needs?",{"text":80,"@type":76},"They mainly support unimodal keyword matching and use outdated keyword-matching models, which cannot retrieve sufficiently interesting and useful results as data volume increases.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach does the research propose to improve retrieval quality?",{"text":84,"@type":76},"It studies cross-media science and technology information retrieval systems based on deep semantic features, integrating machine-learning 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