Lithology identification is a vital basic work in the field of oil and gas exploration and development. Tight sandstone reservoirs suffer from complex rock composition，diverse lithology，and limited lithology identification by conventional logging. As machine learning is powerful in data analysis，this paper proposed a gradient boosting decision tree GBDT）algorithm with strong generalization ability to cut down large manpower and material resource consumption in lithology identification. Taking Chang7 Member of Yanchang Formation in Ordos Basin as the research object，it selects eight logging parameters including acoustic time difference（AC），natural gamma ray（GR），resistivity（RT），clay content（SH），natural potential（SP），effective porosity（POR），water saturation（Sw），and density（DEN）through sensitive analysis to build a lithology identification model based on GBDT algorithm. Actual data were applied to verify and analyze the application effect. The accuracy of the GBDT algorithm can reach 92%，compared with that of other methods such as naive Bayes，random forest，support vector machine，and artificial neural network for lithology identification. The high-precision lithology identification model based on the GBDT algorithm provides a new solution for tight sandstone reservoir evaluation.