In view of the problems of complex geological exploration coring conditions， large drilling depth， high accident rate， complex lithology， difficulty in obtaining drilling parameters， and inability to know drilling status in real time， with full investigation of existing working condition identification technologies in terms of collection principle， functional types and field measurement， a scheme for real-time measurement of drilling parameters is designed by the methodology of combining hardware and software in reference to the development direction of intelligent and automatic drilling. In this paper， the density clustering method of DBSCAN （clustering algorithm based on distribution density for noise space） is proposed for the first time to analyze drilling parameters， and to determine working conditions jointly with the positive and negative increment of the photoelectric encoder， so as to obtain the bit position and drilling depth. This scheme can distinguish drilling state， obtain drilling process parameters， and invert the formation with the measured data as drillable parameters of the drilled formation. Research on automatic and intelligent drilling rig can obtain a large amount of formation data， which is beneficial to promote the development of drilling technology and realize the breakthrough from experience drilling to intelligent drilling. At the same time， it lays a foundation for the intelligent drilling system.