Abstract: Fully homomorphic encryption (FHE), which enables computation on encrypted data without decryption throughout the entire processing flow, offers a promising solution for privacy preservation in cloud computing and other distributed environments. However, the practical deployment of FHE remains significantly constrained by its high computational complexity, poor data locality, and limited parallelism. Among the core operations in FHE, the number theoretic transform (NTT) plays a pivotal role in determining overall system performance. This paper targets the butterfly computation pattern, which is central to the NTT algorithm, and proposes a high-efficiency NTT accelerator architecture based on a dataflow computing model. First, we design an RVFHE extension instruction set tailored for NTT butterfly operations, incorporating custom modular multiplication and modular addition/subtraction units to enhance the efficiency of modular arithmetic. Second, we introduce a novel NTT data reordering scheme, combined with a structured butterfly address generation strategy, to reduce the control complexity and access conflicts associated with cross-row and cross-column data exchanges. Finally, we develop a dataflow-driven NTT accelerator architecture that leverages data dependency-triggered execution to enable efficient on-chip scheduling and data reuse, thereby exploiting instruction-level parallelism to the fullest extent. Experimental results demonstrate that, compared to NVIDIA GPU, the proposed architecture achieves up to 8.96× speedup and 8.53× improvement in energy efficiency. Furthermore, compared to state-of-the-art dedicated NTT accelerators, our design delivers a 1.37× performance gain.