Children’s stories play a crucial role in early cognitive and language development, yet existing story generation models lack the safety and age-appropriateness required for educational applications, especially in non-English languages. We introduce CCStories, a curated dataset of 22,200 high-quality Chinese children’s stories designed specifically to support safe and appropriate AI-generated content for young readers. Unlike general-purpose datasets derived from adult-oriented platforms, CCStories targets three critical dimensions of child-appropriate text: readability, syntactic complexity, and toxicity. We further propose a multi-dimensional evaluation framework that jointly assesses appropriateness and generation quality. As a validation of the dataset’s utility, we fine-tune five representative backbones: CPM, PanGu-𝛼, GLM-4, Qwen, a Chinese-adapted LLaMA-3 variant (Gigi-Llama-3-8B-zh), and additionally benchmark GPT-5-mini as a zero-shot instruction-tuned reference. Fine-tuning reduces offensive-prompt toxicity by up to 26.7 percentage points and raises readability by up to 14.3 percentage points, narrowing much of the gap to the GPT-5-mini reference. A human evaluation confirms that appropriateness rises from 53% to 86% after fine-tuning, and human validation of the toxicity classifier shows that the model ranking it induces matches the human ranking. CCStories provides a publicly available resource for building and benchmarking safer, more age-appropriate Chinese generative models for children’s education.