Automatic instruction generation offers a low-cost, high-efficiency pathway for fine-tuning large language models (LLMs). However, existing methods struggle in knowledge-intensive domains and complex reasoning tasks due to their dependence on high-quality seed data, limited coverage of single-document knowledge, and repetitive content. To overcome these limitations, this paper presents GCIG, a GraphRAG-based Cross-document Instruction Generation framework. We begin by constructing an enhanced knowledge graph to provide a structural representation of the raw corpus, followed by LLM-driven selection of reliable subgraph-text pairs based on factuality and logical complementarity. Subsequently, we adaptively generate diverse questions through task-aware prompts and context-sensitive retrieval. Finally, we employ Chain-of-Thought reasoning to anchor entity paths and integrate scattered evidence, thereby closing logical gaps and improving answer coherence. Experiments on knowledge-intensive and multi-hop question-answering tasks demonstrate that GCIG outperforms existing methods, producing instruction data with stronger logical consistency and broader knowledge coverage for effective LLM fine-tuning. The code and data are publicly available at https://github.com/WhitEiller/GCIG.