As the traditional inversion algorithms for particle size distribution measurement by dynamic light scattering show complex computation, lower accuracy and poorer anti-noise capacity, this paper proposes a soft sensing method for particle size distribution based on improved Bagging algorithm by using idea big data. The data of autocorrelation function and particle sizing distribution were obtained by changing the parameters of particle distribution shape. Then the learning machines were trained by the data. Finally, the traditional Bagging algorithm was improved on the basis of the character of high dimensional data. The improved Bagging strategy was used to aggregate the machines for bettering the model accuracy and its generalization performance. A validation experiment was performed by simulating the single peak data and soft sensing for the standard particles with a diameter of 300 nm. Experiment results demonstrate that the proposed method predicts the peak position and the width of particle sizing distribution accurately, and the best accuracy of peak position measurement is 1 nm. Meanwhile, the accuracies for standard particles with diameters of 300 nm and 503 nm are 3 nm and 4 nm, respectively. The proposed method provides a new way for the particle size distribution measurement in dynamic light scattering.