In recent years, micro-acoustic devices, such as surface acoustic wave (SAW) devices, and bulk acoustic wave (BAW) devices have been widely used in the areas of Internet of Things and mobile communication. With the increasing demand of information transmission speed, working frequencies of micro-acoustic devices are becoming much higher. To meet the emerging demand, Lamb wave devices with characteristics that are fit for high working frequency come into being. However, Lamb wave devices have more complicated vibrating modes than SAW and BAW devices. Methods used for SAW and BAW devices are no longer suitable for the mode extraction of Lamb wave devices. To solve this difficulty, this paper proposed a method based on machine learning with convolutional neural network to achieve automatic identification. The great ability to handle large amount of images makes it a good option for vibrating mode recognition and extraction. With a pre-trained model, we are able to identify and extract the first two anti-symmetric and symmetric modes of Lamb waves in varisized plate structures. After the successful use of this method in Lamb wave modes automatic extraction, it can be extended to all micro-acoustic devices and all other wave types. The proposed method will further promote the application of the Lamb wave devices.