Test-time adaptation (TTA) methods effectively address domain shifts by dynamically adapting pre-trained models to target domain data during online inference. While effective for 2D images, TTA struggles with 3D point clouds due to their irregular and unordered nature. Existing 3D TTA methods often involve complex high-dimensional optimization tasks, such as patch reconstruction or per-point transformation learning in the spatial domain, which require access to additional training data. In contrast, we propose Graph Spectral Domain Test-Time Adaptation (GSDTTA), a novel approach for 3D point cloud classification that shifts adaptation to the graph spectral domain, enabling more efficient adaptation by capturing global structural properties with fewer parameters. Point clouds in target domain are represented as outlier-aware graphs and transformed into graph spectral domain by Graph Fourier Transform (GFT). For efficiency, we only optimize the lowest 10\% of frequency components, which capture the majority of the point cloud’s energy. An inverse GFT (IGFT) is then applied to reconstruct the adapted point cloud with the graph spectral-driven point shift. Additionally, an eigenmap-guided self-training strategy is introduced to iteratively optimize both spectral adjustment and model parameters. Experimental results and ablation studies on benchmark datasets demonstrate the effectiveness of GSDTTA, outperforming existing TTA methods for 3D point cloud classification.