Abstract
<p>Large amounts of noise and a lack of contextual domain knowledge lead to slow and inefficient cross-domain image learning. This paper proposes an image scenario spatial data classification model based on evidence-based behavioral logic, intervenes in image annotation through evidence-based dynamic knowledge graphs, and uses spatial similarity measurement to evaluate the effectiveness and robustness of the method. The results show that: 1) Organizing the dynamic knowledge graphs of contextual domain knowledge by behavioral logic can significantly improve the association efficiency of each model. 2) The calculation method of image scenario space comparison based on behavior evidence can decrypt the implicit knowledge of images and significantly improve the effectiveness of image scenario space interpretation. The research results are helpful to guide the design and implementation of cross-domain image interpretation systems and improve the efficiency of information sharing.</p> <p>&nbsp;</p>
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