Knowledge graphs are widely used in applications such as question answering and recommendation systems, where multi-hop reasoning is a key task. Existing methods often overlook relational semantics, which limits their performance in complex reasoning tasks. While LLMs offer strong semantic capabilities, current approaches fail to simplify reasoning paths and make limited use of semantic cues. To address these limitations, we propose KGMRF-LRPM (Knowledge Graph Multi-hop Reasoning Framework based on LLM and Relation Path Matching), which divides the reasoning process into three stages: relation semantic enhancement, reasoning path planning, and relation path matching. The framework leverages prompt-based LLM collaboration and relation path alignment to improve reasoning efficiency and accuracy. Experimental results on three benchmark knowledge graph datasets demonstrate that KGMRF-LRPM achieves better performance than existing methods, including GQE, Q2B, PERM, TOG, and ROG, in terms of accuracy and generalization.