A clustering fusion method based on user electrical load data subdivision comprises the following steps of (1) collecting data, (2) modifying the data, (3) converting the data, (4) standardizing the data, (5) constructing a pretreatment clustering algorithm set, (6) building a consensus matrix, (7) running the clustering fusion method, and (8) collecting users. Due to the facts that results obtained in different clustering algorithms or in the same clustering algorithm by using different parameters are automatically combined, and a best clustering result can be automatically judged and generated through clustering fusion, the clustering fusion method based on the user electrical load data subdivision has the advantages of improving self-adaption processing capability of a clustering analysis model, reducing dependency degree to priori knowledge in a user electrical load data clustering analysis process, reducing manual operations, and improving automatic degree of the clustering fusion method based on the user electrical load data subdivision.