Aiming at inefficient clustering due to dimensional disaster and loss of feature information in text clustering， this paper proposes a clustering algorithm based on feature matrix optimization and improved principal component analysis （PCA） dimensionality reduction. On the basis of the original term frequency inverse document frequency （TF-IDF） algorithm， an adaptive length frequency weight （ALFW） optimization scheme is proposed， which makes the distribution of the feature matrix better and the characterization of the feature terms more obvious. In the process of dimensionality reduction， the PCA algorithm is optimized by using the joint entropy standard in information theory， and the UE-PCA （United entropy-PCA） algorithm is proposed to further reduce the dimensionality of sparse high-dimensional data and better retain the authenticity of the original high-dimensional data. Simulation experiments show that the proposed algorithm （K-means + UE-PCA + ALFW） achieves better performance than other similar algorithms.