Knowledge Graph-enhanced Large Language Models (KG-Enhanced LLMs) integrate the linguistic capabilities of LLMs with the structured semantics of Knowledge Graphs (KGs), showing strong potential in knowledge-intensive reasoning tasks. However, existing methods typically adopt query-driven iterative reasoning from a local perspective, which limits their ability to capture semantically distant but crucial information, leading to dual bottlenecks in efficiency and accuracy for complex multi-hop tasks. To address this issue, we propose MIAoG, a multi-view instructed adaptive reasoning of LLM on KG, which is designed to overcome the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. Instead of query-anchored exploration, MIAoG first prompts the LLM to generate a multi-view instruction set that outlines diverse potential reasoning paths and explicitly specifies global reasoning intentions to guide the model toward coherent and targeted reasoning. During reasoning, MIAoG integrates a real-time introspection mechanism that evaluates the alignment between the current path and the instructions, adaptively pruning inconsistent trajectories to enhance global consistency while maintaining efficiency. Extensive experiments on multiple public datasets show that MIAoG achieves state-of-the-art performance in KG-enhanced LLM reasoning, particularly excelling in complex multi-hop scenarios.