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Due to unclear information expression and excessive knowledge redundancy, learners face challenges in identifying relevant knowledge. Furthermore, the presence of substantial unstructured knowledge in subject domains also hinders the effective transmission and application of knowledge. To address these issues, a framework for constructing a subject domain knowledge graph is proposed in this work. The framework primarily aims to visualize isolated information and connect knowledge into graph structures. The knowledge graph can help learners quickly and efficiently acquire the knowledge they need. The novel framework is constructed with three steps. The first step is to design the ontology rules based on the domain-specific subject knowledge from the perspective of classification, and also to construct the schema layer of the knowledge graph. The second step is to propose a domain-optimized BiLSTM-ResNet-CRF model for subject domain entity recognition, which introduces residual blocks to enhance fine-grained local contextual feature extraction for multi-word technical terms, addressing the limitations of traditional BiLSTM-CRF models in educational text processing. The BERT relation extraction model is used to extract relations between knowledge entities. Then the data layer is constructed. Finally, the third step is to achieve knowledge fusion through entity linking and two-layer entity alignment against results stored in a database. The result comparisons on the dataset show that the novel BiLSTM-ResNet-CRF model has higher scores than several other classical models, achieving an F1-score of 80.26%. The proposed framework\u2019s effectiveness is rigorously validated using high school mathematics as a representative case study with a well-structured knowledge system.<\/jats:p>","DOI":"10.3390\/systems14060623","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:17:31Z","timestamp":1780467451000},"page":"623","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BiLSTM-ResNet-CRF: An Improved Model for Subject Knowledge Graph Construction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1703-3330","authenticated-orcid":false,"given":"Yinghong","family":"Ma","sequence":"first","affiliation":[{"name":"Business School, Shandong Normal University, Jinan 250014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6172-6339","authenticated-orcid":false,"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Business School, Shandong Normal University, Jinan 250014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0847-5962","authenticated-orcid":false,"given":"Zhiyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Business School, Shandong Normal University, Jinan 250014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5676-7126","authenticated-orcid":false,"given":"Shengyao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Business School, Shandong Normal University, Jinan 250014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9425-9395","authenticated-orcid":false,"given":"Le","family":"Song","sequence":"additional","affiliation":[{"name":"Business School, Shandong Normal University, Jinan 250014, China"},{"name":"School of Business Administration, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105451","DOI":"10.1016\/j.autcon.2024.105451","article-title":"From data to knowledge: Construction process analysis through continuous image capturing, object detection, and knowledge graph creation","volume":"164","author":"Pfitzner","year":"2024","journal-title":"Autom. 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