Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative analysis method of pleural line. At first, the novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to evaluate the lesion degree of pleural line in LUS images. We prospectively evaluated 5390 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the extraction and assessment effect has reached 0.87 (i.e., Dice) and 94.47% (i.e., Accuracy), respectively, and the comparison with previous methods found statistical significance (P<0.001 for all). Thus, the proposed method has great application potential for assessment of pleural line on LUS images and aid lung disease diagnosis and treatment.