Automatic portrait matting methods aim to accurately separate foreground portraits by predicting alpha mattes without auxiliary guidance. Recent portrait matting methods only rely on spatial features for feature extraction, which leads to detail loss and background noise interference. To address these issues, we propose an automatic portrait matting method called HarmonyMatting, based on the harmony of frequency-spatial domain information. Firstly, we employ a Spatial-Frequency Pyramid module (SFP) with a multi-scale frequency band decomposition strategy to enhance the model’s multi-granularity perception of the foreground target. Secondly, we design a Harmony Transformer Block (HTB) to model semantic features in the spatial domain while realizing background noise suppression in the frequency domain. Thirdly, we introduce a Cross-domain Feature Reconstruction module (CFR) to facilitate bidirectional information transfer between the frequency and spatial domains, reducing the detail loss during upsampling. Extensive experiments on Human-2K and P3M-10k datasets demonstrate that HarmonyMatting can effectively suppress background noise interference while preserving rich fine details.