Lithology identification is basic and important for geological work. Traditional lithology identification method is overwhelmingly dependent on manual experience and geological expertise, which is time- consuming and highly professional. Besides, it is vulnerable to subjective factors, resulting in unsatisfactory accuracy. We review the traditional lithology identification methods, then summarize the latest intelligent identification methods. Finally, we detailed introduce the intelligent identification methods based on rock images, microscopic images, images and element data fusion. The accuracy of intelligent lithology identification based on images can reach more than 90%. The lithology identification based on images and element data fusion can alleviate the influence of factors on accuracy such as high image similarity and apparent characteristics of weathering damage. We deem that the current research on intelligent lithology identification is still in its infancy and cannot meet the engineering needs. Combining the advantage of various data sources and using machine learning to deeply mine the internal correlation between rock elements, minerals, spectra and apparent features is conducive to breaking through the limitation of single source information and realizing rapid and accurate lithology identification.