Accurate medical image segmentation is crucial for early diagnosis in clinical medicine. However, neural networks for medical segmentation often overlook the combination of frequency and spatial domains, and the employed attention mechanisms treat each channel neuron equally, forming 1D or 2D weights. Such an approach fails to compute true 3D weights effectively. The SimAM paper mentions 3D attention, but its final expression formula suggests that the results are related only to the global mean and variance, without considering local information. We propose a multi-frequency attention model in multi-scale parameter-free attention (LungSSFNet) for lung segmentation in cystic adenocarcinoma datasets to address these challenges. The proposed model includes three key components: the parameter-free attention mechanism (S), the improved feature concatenation method (U), and the multi-scale, multi-frequency attention module (SSF). The U component improves upon traditional feature concatenation by providing a more effective method of capturing differences between deep semantic and shallow features. Thirdly, the SSF component is a multi-scale, multi-frequency attention module based on parameter-free 3D weights. This provides module that can capture the contours of small targets and tissue boundaries significantly. To ensure that the optimal model is not solely a result of parameter tuning, we leverage the automatic configuration module of nnU-Net to determine the parameters. These parameters will remain fixed during subsequent model evaluation. Through extensive experiments, we demonstrated that LungSSFNet consistently outperforms the state-of-the-art models by 1–2% in the segmentation of cystic adenocarcinoma. Our LungSSFNet code is available at https://github.com/zx0412/LungSSFNet .