Dynamic point clouds have significant value in cutting-edge applications such as immersive communication and autonomous driving, and their efficient compression is key to achieving real-time transmission and storage. Although rule-based and learning-based approaches have made progress in point cloud geometry compression, existing methods are still insufficient in leveraging inter-frame correlations in dynamic sequences. This paper proposes a dynamic point cloud geometry compression method based on feature-domain nearest neighbor search and concatenation, extending the multi-scale sparse representation framework to dynamic scenes, and introducing multi-scale temporal priors to enhance inter-frame conditional coding. Specifically, by extracting hierarchical features from the reconstructed reference frames and performing nearest-neighbor search and concatenation with the current frame features in the feature domain, spatiotemporal contextual information across spaces is constructed, thereby enabling more accurate estimation of voxel occupancy probabilities. This method transmits only partial features at the encoding end, and the decoding end reconstructs the temporal priors using reference frame information, significantly improving compression efficiency. The experiment was conducted on a standard dataset following the MPEG general test conditions. The results indicate that the method proposed in this paper achieves significant BD-Rate gains of over 10% in terms of D1-PSNR and D2-PSNR on multiple test sequences compared to existing rule-based and learning-based compression methods, particularly demonstrating superior rate-distortion performance across a wide range of bitrates. The test results validate the effectiveness and advancement of the algorithm proposed in this paper for dynamic point cloud geometry compression.