用AI打造符合伊斯兰教法的聊天机器人，解答宗教疑问。
Implementing a Sharia Chatbot as a Consultation Medium for Questions About Islam
- 结合强化学习与语义嵌入技术，实现精准问答。
- 在2.5万条真实经文数据上测试，语义准确率达87%。
- 适合想便捷获取权威伊斯兰知识的人群使用。
本研究实现了基于伊斯兰教法的聊天机器人，作为咨询伊斯兰问题的交互式工具，采用强化学习（Q-Learning）与Sentence-Transformers进行语义嵌入，确保回答的上下文相关性和准确性。系统遵循CRISP-DM方法论，使用来自《古兰经》、圣训及学者裁决的25,000条问答对构成的精选数据集，以JSON格式存储，具备灵活性与可扩展性。后端基于Flask API开发，前端采用Flutter构建移动应用原型，在涵盖教法、信仰、礼拜与人际事务等多领域测试中达到87%的语义准确率，展现出提升宗教素养、推动数字传教及提供可信伊斯兰知识的潜力。尽管在封闭域查询中表现良好，但存在静态学习与数据依赖等局限，未来可拓展持续学习与多轮对话能力，使传统伊斯兰学术与现代人工智能咨询形成有效衔接。
原文摘要 · Abstract (English)
This research presents the implementation of a Sharia-compliant chatbot as an interactive medium for consulting Islamic questions, leveraging Reinforcement Learning (Q-Learning) integrated with Sentence-Transformers for semantic embedding to ensure contextual and accurate responses. Utilizing the CRISP-DM methodology, the system processes a curated Islam QA dataset of 25,000 question-answer pairs from authentic sources like the Qur'an, Hadith, and scholarly fatwas, formatted in JSON for flexibility and scalability. The chatbot prototype, developed with a Flask API backend and Flutter-based mobile frontend, achieves 87% semantic accuracy in functional testing across diverse topics including fiqh, aqidah, ibadah, and muamalah, demonstrating its potential to enhance religious literacy, digital da'wah, and access to verified Islamic knowledge in the Industry 4.0 era. While effective for closed-domain queries, limitations such as static learning and dataset dependency highlight opportunities for future enhancements like continuous adaptation and multi-turn conversation support, positioning this innovation as a bridge between traditional Islamic scholarship and modern AI-driven consultation.
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