Optical flow, the process of predicting motion fields from image sequences, is a fundamental problem in computer vision. While recent methods (represented by RAFT) leveraging 4D cost volume and iterative refinement have achieved state-of-the-art performance, two critical limitations persist: (i) the simplistic construction method for 4D cost volume fails to preserve semantic information in images, and (ii) the zero-initialized flow estimation necessitates excessive iterations (typically 32 iterations). To address these challenges, we propose AMFlow, a novel framework that integrates an attention-based cost volume and matching initialization. Specifically, AMFlow improves accuracy and efficiency by refining the initial cost volume and leveraging high-quality initial flow fields, achieving competitive or superior results with only 8 iterations, compared to RAFT-style models requiring 32 iterations. Additionally, extensive experimental results show our method maintains a balance between accuracy and computational efficiency regarding both inference time and GPU memory usage. On the Sintel benchmark, our method achieves competitive performance, with 1.19px and 2.67px average end-point error (AEPE) on the clean and final passes.