The clustering technique in data mining has been widely applied for load curves clustering. Load curves clustering helps refining common and different characteristics among loads, which has important application values for the practicality of load model. On the other hand, it helps analyzing load patterns, guiding planning and real-time dispatching of power systems. In this paper, an adaptive k-means++algorithm is proposed, which synthesizes results of different cluster numbers to verify the similarity of the samples in the dataset, and adopts an iterative graph-partitioning method to identify the optimal cluster number. The improved algorithm avoids excessive deviation of single clustering result caused by inappropriate cluster number of daily load curves, which could improve the accuracy of load curves classification. Numerical experiments verify the feasibility and effectiveness of the proposed algorithm, and show that the accuracy of algorithm is high and robustness is good when solving the best cluster number.