Focusing on the specific information of the positional relationship between objects in the image, a neural network image summary generation model integrating spatial relationship mechanism is proposed, in order to provide key information (object position or trajectory) for downstream tasks such as visual question answering and voice navigation. In order to enhance the learning ability of the positional relationship between objects of the image encoder, the geometric attention mechanism is introduced by improving the Transformer structure, and the positional relationship between objects is explicitly integrated into the appearance information of the objects. In order to assist in the completion of specific information-oriented extraction and summary generation tasks, a data production method for relative position relations is further proposed, and the image abstract data set Re-Position of the position relations between objects is produced based on the SpatialSense data set. The experimental results of comparative evaluation with five typical models show that the five indicators of the proposed model are better than those of other models on the public test set COCO, and all six indicators are better than those of other models on Re-Position data set.