Load node classification based on daily load curve is an important part of load modeling. The detailed and appropriate classification results retain the internal characteristics of load nodes and can improve the efficiency of power system simulation calculation. At present, the node clustering method based on artificial intelligence has made rapid progress. However, the overall adaptability to data deep feature extraction is still insufficient. This paper presents the daily load curve clustering method based on the improved deep embedded algorithm, which uses the ability of neural network to effectively extract the deep features of the data. Then, an improved method of increasing the dimension first and then clustering is proposed. Through the comparative analysis of numerical examples, the feasibility of the proposed algorithm and the correctness of the improved dimension reconstruction clustering method are verified.