Ultrasonic imaging is convenient and safe for cardiovascular disease diagnosis. Speckle tracking can obtain accurate myocardial movement data and provide important information for the diagnosis of cardiac function. Block matching method and optical flow method are the most commonly used speckle tracking methods. However, the accuracy of these methods cannot meet the needs of clinical application. Deep learning is applied to speckle tracking technology. Based on the correlation filters given to the deep convolution network, the migration learning method is introduced to obtain the feature mapping on the convolution layer on the pre-trained ImageNet VGG19 network. The feature mapping is used as the training data of correlation filters, and the tracking results obtained from convolution layers with different depths are filtered frame by frame, giving different weights to obtain the optimal tracking position within a certain search range. Then the correlation filter is updated to track the myocardial motion. The proposed deep learning based method has better accuracy for myocardial motion tracking, which indicates that the target tracking method based on convolutional neural network has potential advantages in this field.