Abstract: To address the underutilization of spatiotemporal features in event data, a spatiotemporal-aware event rep-resentation fusion method is proposed for human pose estimation. The method constructs spatiotemporal event frames by in-tegrating timestamp encoding and event counting, preserving temporal dynamics; then designs a spatiotemporal feature extraction module with 3D convolutions and temporal attention to hierar-chically capture motion patterns; finally adopts dynamic weighting to adaptively fuse multi-scale features for joint localization. Experiments on DHP19 dataset demonstrate a mean joint error of 5.56, outperforming baselines by 1.82, proving the efficacy of spatiotemporal fusion.