The measurement by fixed point of ultrasound image is very important in clinical medicine. Because of the large speckle noise and blurred edges in ultrasound images, landmark detection in echocardiography is quite challenging. Meanwhile, current landmark detection algorithm optimizes a single landmark position, and it's difficult to get accurate distance with guaranteeing the accuracy of each landmark. To get more accuracy result of landmark and the distance between two landmarks in ultrasound, cascaded convolution neural network is proposed to automatic detection landmark in echocardiography, our framework adopts two stages of carefully designed deep convolution networks that predict landmark location in a coarse-to-fine manner. Firstly, networks at the first level estimate positions of two landmarks coarsely, the patch includes two landmarks as input of the second stage. Then a loss function with distance correction term is proposed to optimize the second network, and gets the final landmark position based on the output of first stage. Experimental results show that compared to traditional network and regression tree, the proposed method could not only guarantee the accuracy of landmark position but also increase the accuracy of distance, compared with traditional cascaded convolution neural network, and the accuracy of distance is nearly 30% higher than the traditional method.