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However, rational design remains challenging due to complex structure\u2013property relationships involving multiple formulation parameters. This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. XGBoost demonstrated superior performance after Optuna-based optimization, achieving R2 values of 0.964 for training and 0.801 for testing. SHAP analysis provided unprecedented mechanistic insights, revealing that PVA molecular weight dominates mechanical performance (SHAP importance: 84.94) through chain entanglement and crystallization mechanisms, followed by degree of hydrolysis (72.46) and cross-linking parameters. The interpretability analysis identified optimal parameter ranges and critical feature interactions, elucidating complex non-linear relationships and reinforcement mechanisms. By addressing the \u201cblack box\u201d limitation of machine learning, this approach enables rational design strategies and mechanistic understanding for next-generation multifunctional hydrogels.<\/jats:p>","DOI":"10.3390\/gels11070550","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T15:48:22Z","timestamp":1752680902000},"page":"550","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Interpretable Prediction and Analysis of PVA Hydrogel Mechanical Behavior Using Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Liying","family":"Xu","sequence":"first","affiliation":[{"name":"School of Food Engineering, Harbin University, Harbin 150086, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anqi","family":"Lin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zichuan","family":"Su","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3220-0555","authenticated-orcid":false,"given":"Daxin","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11585","DOI":"10.1021\/acs.chemrev.4c00513","article-title":"Wearable and Implantable Soft Robots","volume":"124","author":"Yin","year":"2024","journal-title":"Chem. 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