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A teaching quality evaluation system is constructed using teaching content, teaching methods, and teaching outcomes. Experimental results show that the HMIPSO-BP model outperforms the traditional BP neural network model, with the mean squared error reduced by approximately 27.8% and the minimum error decreased by approximately 28.2%. This approach significantly reduces computation time while maintaining evaluation accuracy. The proposed method provides a novel and effective technical pathway for monitoring and improving the quality of English teaching in higher education.<\/jats:p>","DOI":"10.20965\/jaciii.2026.p0558","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:02:06Z","timestamp":1773932526000},"page":"558-565","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Evaluation Algorithm for English Teaching Quality Based on HMIPSO-Optimized BP Neural Networks"],"prefix":"10.20965","volume":"30","author":[{"given":"Feng","family":"Liu","sequence":"first","affiliation":[{"name":"Maritime College, Hainan Vocational University of Science and Technology, No.18 Qiongshan Avenue, Meilan District, Haikou City, Hainan 571126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p0558-1","doi-asserted-by":"crossref","unstructured":"M. \u010euri\u0161ov\u00e1, A. 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