Human pose estimation is a critical task in computer vision and High-Resolution Networks （HRNets） are widely used due to their outstanding performance. However， improvements in model accuracy are often accompanied by a surge in parameter count and computational cost. To address this issue， an innovative lightweight improved module， Frequency Domain Cross Attention Convolution （FCAC）， was proposed. First， the basic convolutional module was reconstructed by fusing Wavelet Transform Convolutions （WTConvs） with Depthwise Separable Convolutions （DSCs） with WTConv's enhancing receptive field capability compensating for DSC's lack of contextual connections， thereby optimizing HRNet's performance while maintaining low complexity. Second， an attention mechanism was employed to guide low-frequency components to learn high-frequency components， and the learning results were aggregated dynamically through a dynamic channel reweighting mechanism. Furthermore， Squeeze-and-Excitation Network （SENet） was used to compensate for the current WTConv's learning of inter-channel feature relationships. Finally， FCAC was deployed at key nodes in the backbone network. Experimental results on the COCO dataset show that although the number of parameters of the improved HRNet-W32 model is increased by 22%， the model has the floating-point operations reduced by 7.0%， the mean Average Precision （mAP） improved by 3.5 percentage points， and the convergence ability greatly enhanced.