The open-world scene graph generation task has difficulty in detecting unknown objects and their relationships. To address this issue, a relation-reasoning model based on multidimensional feature fusion (MDFF) is proposed. The proposed model is combined with an open-world object detector to form a two-stage open-world scene graph generation algorithm. First, the pretrained open-world object detector identifies objects in the input images. The MDFF model then performs relationship inference based on the detection results. Comparative experiments are conducted on the VG 150 dataset using traditional methods and the MDFF model. The experimental results indicate that the MDFF model achieves 7% improvement in recall rate for predicate classification tasks. Moreover, the performance improves by 3% in open-world scene graph generation and zero-shot inference tasks. Furthermore, ablation studies have confirmed the effectiveness of different feature dimensions on model performance improvement.