Obtaining reliable labels is time-consuming and laborious caused by the characteristics of ultrasonic images such as strong noise， low quality and blurred boundary. Therefore， a semi-supervision and multi-scale cascaded attention based ultrasound carotid plaque segmentation method was proposed. Firstly， a semi-supervised segmentation method of Uncertainty Rectified Pyramid Consistency （URPC） was used to make full use of unlabeled data to train the model， so as to reduce the time-consuming and laborious labeling pressure. Then， a dual encoder structure based on edge detection was proposed， and the edge detection encoder was used to assist the ultrasonic plaque image feature encoder to fully acquire the edge information. In addition， a Multi-Scale Fusion Module （MSFM） was designed to improve the extraction of irregularly shaped plaques by adaptive fusion of multi-scale features， and a Cascaded Channel Spatial Attention （CCSA） module was combined to better focus on the plaque region. Finally， the proposed method was evaluated on the ultrasonic carotid plaque image dataset. Experimental results show that the Dice index and IoU （Intersection over Union） index of the proposed method on the dataset are 2.8 and 6.3 percentage points higher than those of the supervised method CA-Net （Comprehensive Attention convolutional neural Network） respectively， and 1.8 and 1.3 percentage points higher than those of the semi-supervised method Cyclic Prototype Consistency Learning （CPCL） respectively. It can be seen that this method can effectively improve the segmentation accuracy of ultrasound carotid plaque image.