Lithology identification technology is a powerful means to distinguish the type of target rock, therebymastering the geological structural characteristics and mineral distribution. Traditional lithology identification technolo‑gies（such as rock hand specimen/thin section microscopic identification, remote sensing, and logging methods）havelimitations such as strong subjectivity, low efficiency, weak anti‑interference ability, and complex processes. Althoughrock lithology automatic identification methods based on traditional machine learning have broken through the bottle‑necks of traditional lithology identification technology to some extent, the rich lithology feature information is not fullyutilized. Smart lithology identification technology based on deep learning has gradually shown excellent performance.Based on existing deep learning models, this paper introduces the technical process of using the VGG-16 convolutionalneural network to recognize lithology through transfer learning, which not only reduces the dependency on the amountof lithology annotation data but also increases the generalization ability of identification by automatically capturing thehigh‑dimensional feature information of lithology. In addition, the development and innovation of smart lithology identi‑fication technology will be carried out in the following directions: constructing a standard rock image database containingmultiple ore types and multiple scales; developing an on‑site real‑time lithology identification system with lightweightprocessing of identification models; integrating multi‑dimensional data information such as rock vision, spectral, andphysical properties, and improving the reliability of intelligent identification models through collaborative analysismethods; and constructing a mine lithology dynamic sensing system to promote the smart upgrading of the wholeprocess of precision mining.