Abstract: Traditional principal components analysis (PCA) was improved through adding weight to standardizing data matrix and using absolute value of eigenvector, and the comprehensive evaluation model of engineering materials was established on the base of improving traditional PCA. Taking the comprehensive evaluation of low temperature storage pot materials, 5 kinds of aluminum alloys, as an example, the applicability of the model mentioned above was studied. The results show that aluminum alloy 2014-T6 was the best candidate material, which accorded with industry practice and the result obtained by TOPSIS (a technique for order preference by similarity to solution). Through taking the absolute value of the feature vector, improving PCA avoided the negative evaluation results and made them more reasonable. All above showed that improving PCA was feasible in the comprehensive evaluation of engineering materials.