Cross-domain　relation　extraction　has　become　an　essential　approach　when　target　domain　lacking　labeled　data.　Most　existing　works　adapted　relation　extraction　models　from　the　source　domain　to　target　domain　through　aligning　sequential　features,　but　failed　to　transfer　non-local　and　non-sequential　features　such　as　word　co-occurrence　which　are　also　critical　for　cross-domain　relation　extraction.　To　address　this　issue,　in　this　paper,　we　propose　a　novel　tripartite　graph　architecture　to　adapt　non-local　features　when　there　is　no　labeled　data　in　the　target　domain.　The　graph　uses　domain　words　as　nodes　to　model　the　co-occurrence　relation　between　domain-specific　words　and　domain-independent　words.　Through　graph　convolutions　on　the　tripartite　graph,　the　information　of　domain-specific　words　is　propagated　so　that　the　word　representation　can　be　fine-tuned　to　align　domain-specific　features.　In　addition,　unlike　the　traditional　graph　structure,　the　weights　of　edges　innovatively　combine　fixed　weight　and　dynamic　weight,　to　capture　the　global　non-local　features　and　avoid　introducing　noise　to　word　representation.　Experiments　on　three　domains　of　ACE2005　datasets　show　that　our　method　outperforms　the　state-of-the-art　models　by　a　big　margin.