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.