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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SSR</journal-id><journal-title-group><journal-title>Scientific and Social Research</journal-title></journal-title-group><issn>2661-4332</issn><eissn>2981-9946</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/ssr.v7i8.11983</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research and Practice of Multi-dimensional Chinese Character Quantification Evaluation Methods</title><url>https://artdesignp.com/journal/SSR/7/8/10.26689/ssr.v7i8.11983</url><author>DengPeng,YangGuiying</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>7</volume><issue>8</issue><history><date date-type="pub"><published-time>2025-09-17</published-time></date></history><abstract>Addressing the pain points of traditional Chinese character evaluation, which is highly subjective and lacks quantitative standards, this paper proposes a three-dimensional intelligent quantitative evaluation model for Chinese characters based on “accuracy—balance—standardization”, transitioning from experience-driven to data-driven evaluation: improving multi-model convolutional neural networks to extract stroke keypoints for accuracy assessment, combining image moments with cosine similarity to measure center of gravity deviation for balance evaluation, and using 3×3 grid pixel density ratios to characterize structural framework for standardization measurement. The model operates in a closed-loop system of “recognition—quantification—fuzzy comprehensive evaluation”, enhancing recognition accuracy through hard voting, automatically calibrating the scoring range using the “best-worst sample set”, and obtaining intelligent weights of 39:32:29 through backpropagation training. Experiments on four types of samples from the CASIA-HWDB1.1 and custom datasets show that the average error between the model and expert scores under intelligent weights is significantly lower than that of the two fixed weights, providing a practical quantitative evaluation tool for Chinese character education and cultural heritage.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Xiao X, Li CC, 2022, Research progress on handwritten Chinese character evaluation methods. 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