Aiming at the problems of medical ultrasound images such as many noisy points, fuzzy boundaries, and difficulty in defining the cardiac contours, a new Spatial Frequency Divided Attention Network for ultrasound image segmentation (SFDA-Net) was proposed. Firstly, with the help of Octave convolution, the high and low-frequency parallel processing of image in the entire network was realized to obtain more diverse information. Then, the Convolutional Block Attention Module (CBAM) was added for paying more attention to the effective information when image feature recovered, so as to reduce the loss of segmenting the entire target area. Finally Focal Tversky Loss was considered as the objective function to reduce the weights of simple samples and pay more attention on difficult samples, as well as decrease the errors introduced by pixel misjudgment between the categories. Through multiple sets of comparative experiments,it can be seen that with the parameter number lower than that of the original UNet++, SFDA-Net has the segmentation accuracy increased by 6.2 percentage points, Dice sore risen by 8.76 percentage points, mean Pixel Accuracy (mPA) improved to 84.09%, and mean Intersection Over Union (mIoU) increased to 75.79%. SFDA-Net steadily improves the network performance while reducing parameters, and makes the echocardiographic segmentation more accurate.