A method for extracting significant texture features of a B-ultrasonic image and application thereof discloses a channel attention mechanism network, i.e. a context activation residual network, which is designed to effectively model the B-ultrasonic liver fibrosis texture information, and which uses the global context information to strengthen important texture features and suppress useless texture features, such that the deep residual network can capture more significant texture information in the B-ultrasonic images. The process can be mainly divided into two phases: training and testing. During the training phase, the context activation residual network may be trained by using the B-ultrasonic image blocks as input and the pathological results of liver biopsy as labels. During the testing phase, the B-ultrasonic image blocks may be input into the trained non-invasive liver fibrosis diagnosis model to obtain the liver fibrosis staging result for each ultrasonic image.