Density peak clustering is a clustering strategy that groups data points based on their density in the datasets, which determines cluster centers by finding density peak points and clustering around these centers. It does not require iterative process, nor does it require the user to input too many parameters, which makes it more efficient and easy to use. However, selecting cluster centers manually by decision graph is a major limitation of the algorithm. In the existing research, automatically generate cluster centers methods were proposed, but it didn’t take the contribution of different distances when calculating the local density. In this paper, fuzzy neighborhood was employed to measure the proximity between data points to automatically identify the cluster centers. We redefine the fuzzy local density and the fuzzy relative distance in density peak clustering based on fuzzy neighborhood, and automatically generate cluster center based on their statistics. Compared to traditional algorithms, this method has not added any additional parameters. To verify the effectiveness of the proposed algorithm, we conducted comparative experiments with existing algorithms.