Abstract: Dynamic Scene Graph Generation (DSGG) aims to comprehensively understand videos by abstracting them into visual triplets $<$\textit{subject}, \textit{predicate}, \textit{object}$>$. Most existing methods focus on capturing temporal dependencies, but overlook crucial visual relationship dependencies between entities and predicates, as well as among predicate subclasses. These dependencies are essential for a deeper contextual understanding of scenarios. Additionally, current approaches do not support end-to-end training and instead rely on a two-stage pipeline, which incurs higher computational costs. To address these issues, we propose an end-to-end \textbf{A}ssociation \textbf{R}easoning \textbf{N}etwork (ARN) for DSGG. ARN leverages CLIP’s semantic priors to model fine-grained triplet cues to generate scene graph. In addition, we design a Predicate Association Parsing (PAP) module that employs a conditional weight mapping mechanism to structure entity and predicate representations. We further introduce a Hierarchical Attention (HA) mechanism to integrate spatio-temporal context with entity and predicate representations, enabling effective associative reasoning. Extensive experiments on the Action Genome dataset demonstrate significant performance improvements over existing methods.