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For such huge data storage and identification, in order to manage such data more efficiently and reasonably, traditional semantic similarity algorithm emerges. However, the accuracy of the traditional semantic similarity algorithm is relatively low, and the convergence of corresponding algorithm is poor. Based on this problem, this paper starts with the conceptual structure of language, analyzes the depth of language structure and the distance between nodes, and analyzes the two levels as the starting point. For the information of a specific data resource description frame type, the weight of interconnected edges is used for impact analysis so as to realize the semantic similarity impact analysis of all information data. Based on the above improvements, this paper also systematically establishes the data information modeling process based on language conceptual structure and establishes the corresponding model. In the experimental part, the improved algorithm is simulated and analyzed. The simulation results show that compared with the traditional algorithm, the algorithm has obvious accuracy improvement.<\/jats:p>","DOI":"10.1155\/2021\/7322066","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T18:35:09Z","timestamp":1621881309000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improvement and Analysis of Semantic Similarity Algorithm Based on Linguistic Concept Structure"],"prefix":"10.1155","volume":"2021","author":[{"given":"Shan","family":"Xiao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2238-4442","authenticated-orcid":false,"given":"Cheng","family":"Di","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0257-2048","authenticated-orcid":false,"given":"Pei","family":"Li","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.047"},{"key":"e_1_2_9_2_2","first-page":"14","article-title":"A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain","volume":"8","author":"Sanguri K.","year":"2020","journal-title":"Scientometrics"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.21895"},{"key":"e_1_2_9_4_2","first-page":"1901","article-title":"MeSH annotation of the chicken genome: MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products","volume":"107","author":"Morota G.","year":"2016","journal-title":"Journal of Cell Biology"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106346"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102188"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.104919"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2017.09.014"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13326-017-0119-z"},{"key":"e_1_2_9_10_2","first-page":"102090.1","article-title":"Sentence modeling via multiple word embeddings and multi-level comparison for semantic textual similarity","volume":"55","author":"Tien N. 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