To address the issues of unknown target size, blurred edges, background interference and low contrast in infrared small target detection, this paper proposes a method based on density peaks searching and weighted multi-feature local difference. Firstly, an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference, thereby increasing the probability of capturing real targets in the density peak search. Secondly, a triple-layer window is used to extract features from the area surrounding candidate targets, addressing the uncertainty of small target sizes. By calculating multi-feature local differences between the triple-layer windows, the problems of blurred target edges and low contrast are resolved. To balance the contribution of different features, intra-class distance is used to calculate weights, achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets. The real targets are then extracted using the interquartile range. Experiments on datasets such as SIRST and IRSTD-1K show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.