Academic regulation advising is vital for helping students interpret and comply with institutional policies, yet building effective systems requires domain-specific regulatory resources. To address this challenge, we propose REBot, an LLM-enhanced advisory chatbot powered by CatRAG, a hybrid retrieval–reasoning framework that integrates RAG with GraphRAG. We introduce CatRAG that unifies dense retrieval and graph-based reasoning, supported by a hierarchical, category-labeled knowledge graph enriched with semantic features for domain alignment. A lightweight intent classifier routes queries to the appropriate retrieval modules, ensuring both factual accuracy and contextual depth. We construct a regulation-specific dataset and assess REBot on classification and question-answering tasks, achieving state-of-the-art performance with an F1-score of 98.89%. Finally, we implement a web application that demonstrates the practical value of REBot in real-world academic advising scenarios.