Label Distribution Learning (LDL) fits the situations well that focus on the overall distribution of the whole series of labels. The numerical labels of LDL satisfy the integrity probability constraint. Due to LDL's special label domain, existing label embedding algorithms that focus on embedding of binary labels are thus unfit for LDL. This paper proposes a specially designed approach MSLP that achieves label embedding for LDL by Multi-Scale Locality Preserving (MSLP). Specifically, MSLP takes the locality information of data in both the label space and the feature space into account with different locality granularity. By assuming an explicit mapping from the features to the embedded labels, MSLP does not need an additional learning process after completing embedding. Besides, MSLP is insensitive to the existing of data points violating the smoothness assumption, which is usually caused by noises. Experimental results demonstrate the effectiveness of MSLP in preserving the locality structure of label distributions in the embedding space and show its superiority over the state-of-the-art baseline methods.