The research on data streams clustering has become a focus in the field of data streams mining. Because the number of data streams is too large, and CPU of the computer has limited memory and time, it’s difficult to carry out clustering quickly and effectively. For that problem, we design an improved clustering algorithm for dynamic data streams based on principal component analysis and density. The PDStream algorithm effectively overcomes the shortcomings of the STREAM algorithm controlled by historical data and the CluStream algorithm is difficult to describe non-spherical and out "old data", resulting in huge amount of data. In the course of the experiment, we compare with the STREAM algorithm, the PDStream algorithm shows the superiority of handling mass data and the characteristics of high-quality clustering.