Electrical penetration graph (EPG) technology is an electrophysiological technique used to record the probing and feeding behaviors of herbivorous, piercing-sucking insects. By analyzing EPG waveforms, it is possible to identify different feeding behaviors of insects within different plant tissues. However, because of noise in the EPG waveform data, subtle differences between different types of waveforms, and in the exact time when the waveforms occur, researchers need to spend a lot of time and effort to interpret waveform data. With the development of artificial intelligence technology, particularly the introduction of machine learning and deep learning, automation of recognition and accurate analysis of EPG waveforms has gradually become a reality. Artificial intelligence technology can quickly extract useful information from many complex EPG waveforms and identify the species-specific feeding behaviors of insects, thereby providing technical support for the intelligent development of EPG technology. This article elaborates on progress in research on the automatic recognition of EPG waveforms and associated statistical analysis, and outlines prospects for combining EPG technology with artificial intelligence and the intelligent development of EPG technology.