Through the classification and analysis of load data

the behavior characteristics of power users can be obtained

which provides support for the formulation of demand response strategies and effect evaluation. Firstly

the load data is preprocessed including identifying and processing abnormal numbers

and smoothing to remove burr data. Secondly

with the reason that the fuzzy C-means clustering algorithm is sensitive to the initial cluster center

easy to fall into local optimality

and is greatly affect-ed by noise

the shortest distance clustering method is proposed to provide the initial clustering center for fuzzy C clustering. The efficiency index is used to select the best clustering results of different categories

and the data density is used to identify and eliminate noise points.Finally

through comparison with other methods and cluster analysis of a certain hemp spinning enterprise’s load

the correctness and effectiveness of the improved algorithm is verified.