In view of the fact that the optimization of the mining methods involves the classification and comprehensive evaluation of a multi-indicator system, in this paper, the target structure is simplified with the principal component analysis, combined with clustering analysis, and the principal component cluster analysis method is proposed, and then the mining methods for 15 samples from a mine are optimized based on this method. In the process, to avoid the shortcomings of the traditional principal component analysis and the errors which may occur in applications, the feature extraction of the principal component analysis is improved by means of the equalization and the comprehensive evaluation is improved by making clustering analysis based on the principal component scores. The meaning of the principal component is interpreted clearly. Moreover, the results of the principal component clustering analysis, the first principal component scores and the principal component composite scores are ranked and analyzed. It is shown that the principal component cluster analysis not only can classify the multivariable data reasonably, but also can make a comprehensive assessment of the performance of various types to fully reflect the actual situation of the mine. The final selected mining method sees a remarkable improvement according to industrial tests, which verifies this decision-making method.