In view of the problems associated with the traditional optimization method for seismic data attribute reduction, i.e., the large amount of computation required in the reduction process and the high CPU occupancy rate, in this paper, we propose an attribute reduction and optimization method for massive seismic data based on principal component analysis (PCA). First, we establish a feature matrix of the seismic data based on the characteristics of seismic samples. The features in the matrix are then clustered and arranged in descending order. We then select the first few data as the seismic data attribute feature results and evaluate the classification information of these results. Next, the classification information is modified using the feature integral criterion to obtain the attribute feature nodes of the massive seismic data. We use PCA to label the principal components of the attribute nodes of the seismic data and establish a global optimization of the semi-supervised dimensionality reduction. The dimensionality reduction results are calculated by eigenvalue decomposition, we solved the problem of over-fitting in the attribute reduction process of massive seismic data, and realized the optimization of the attribute reduction of massive seismic data by combining the PCA algorithm with Fisher discriminant analysis. The experimental results show that the proposed method has a high accuracy and contribution rate of attribute feature selection, and the CPU occupation rate is low during the dimensionality reduction process.