Principal component analysis （PCA） is a commonly used method for feature extraction and data dimension reduction. In many applications， the components whose eigenvalues are greater than the average value are retained. However， there is no specific analysis result for the relationship between the number of principal components and the application results. Therefore， an experimental analysis of the difference in selection of PCA threshold is carried out to provide basis for the PCA threshold selection in different applications. The experiment analysis is used to reduce the dimension of handwritten digital sample set MNIST， and different neural networks are constructed according to different thresholds for classification. Furthermore， the change of classification accuracy under different thresholds is analyzed. The experimental results show that when the threshold of PCA is between 79%—81% （dimension is 41—50）， the classification accuracy is the highest， and the accuracy decreases accordingly when the threshold is lower or higher than that region. It is proved that there is no positive correlation between application results and threshold selection of PCA， and the average of the eigenvalues is not a mandatory criterion.