3D　scene　graph　generation　(SGG)　has　been　of　high　interest　in　computer　vision.　Although　the　accuracy　of　3D　SGG　on　coarse　classification　and　single　relation　label　has　been　gradually　improved,　the　performance　of　existing　works　is　still　far　from　being　perfect　for　fine-grained　and　multi-label　situations.　In　this　article,　we　propose　a　framework　fully　exploring　contextual　information　for　the　3D　SGG　task,　which　attempts　to　satisfy　the　requirements　of　fine-grained　entity　class,　multiple　relation　labels,　and　high　accuracy　simultaneously.　Our　proposed　approach　is　composed　of　a　Graph　Feature　Extraction　module　and　a　Graph　Contextual　Reasoning　module,　achieving　appropriate　information-redundancy　feature　extraction,　structured　organization,　and　hierarchical　inferring.　Our　approach　achieves　superior　or　competitive　performance　over　previous　methods　on　the　3DSSG　dataset,　especially　on　the　relationship　prediction　sub-task.