With the rapid development of high-resolution remote sensing technology, aerial image object detection has demonstrated significant application potential in military reconnaissance, urban planning, and other fields. However, current research excessively focuses on model optimization while neglecting systematic exploration of data intrinsic characteristics, particularly the insufficient utilization of spatial prior knowledge such as object orientation distribution. This study innovatively proposes a multi-dimensional EDA analysis framework: first constructing a statistics-based object distribution cognition model to quantify the size-orientation joint distribution characteristics of 18 object categories; secondly developing a background-object correlation matrix to reveal scene semantic constraint patterns; then designing an abnormal region detection algorithm to identify cloud/shadow interference modes; and ultimately establishing an orientation sensitivity analysis model, discovering strong correlations between ship orientations and waterway directions.