The　emergence　of　Large　Language　Models　(LLMs)　has　driven　the　progress　of　deep　learning.　With　the　development　of　LLMs,　they　have　gained　the　ability　to　process　various　types　of　input,　including　images　and　videos.　However,　there　is　still　a　significant　gap　in　LLMs’　understanding　of　graph　structure　data　and　its　inherent　complexity.　Graph　Neural　Networks　(GNNs)　are　a　mature　model　that　is　specifically　designed　as　a　neural　network　model　for　handling　irregular　graph　structure　data.　However,　the　challenge　is　how　to　combine　the　advantages　of　GNNs　and　LLMs,　not　just　relying　on　the　performance　of　GNNs　on　graph　structure　datasets.　Inspired　by　the　progress　of　LLMs,　This　paper　proposes　a　novel　approach　that　integrates　graph　structure　information　with　textual　data,　aiming　to　leverage　the　power　of　LLMs　in　understanding　complex　graph-based　data.　Our　framework　involves　standardizing　both　graph　structures　and　text　datasets　into　a　consistent　length　embedding,　ensuring　compatibility　with　LLM　processing　requirements.　A　lightweight　converter　is　employed　to　forge　links　between　disparate　data　modalities,　preserving　the　integrity　and　characteristics　of　each　original　data　while　converting　them　into　a　unified　representation.　Furthermore,　this　framework　incorporates　Retrieval　Augmented　Thoughts　(RAT)　to　enable　the　effective　integration　of　external　knowledge　sources,　thereby　enriching　the　context　and　coherence　of　generated　responses.　Through　rigorous　evaluation　against　prevailing　benchmarks,　the　proposed　method　showcases　superior　performance　across　four　distinct　datasets,　outperforming　cutting-edge　GNNs　models　currently　dominating　the　field.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.