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Abstract: In this research, an integrated classification method based on principal component analysis– simulated annealing genetic algorithm–fuzzy cluster means (PCA–SAGA–FCM) was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments. A variety of evaluation parameters were selected, including lithology characteristic parameters, poro-permeability quality characteristic parameters, engineering quality characteristic parameters, and pore structure characteristic parameters. The PCA was used to reduce the dimension of the evaluation parameters, and the low-dimensional data was used as input. The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM, the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles. The analysis results of numerical simulation and actual logging data show that: 1) compared with FCM algorithm, SAGA–FCM has stronger stability and higher accuracy; 2) the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership; 3) the results of reservoir integrated classification match well with the lithologic profile, which demonstrates the reliability of the classification method.