In order to solve the problems of low detection accuracy, high false alarm rate and complex calculation of saliency map based on single-class prior knowledge of human visual system detection method in the field of infrared small target detection, a detection method that fuses various characteristics of infrared small targets under complex background conditions is proposed. By fusing the three characteristics of infrared small targets that the local gray value is large, its own gray information conforms to the two-dimensional Gaussian distribution, and the similarity with the neighborhood is low, the saliency map is calculated by covariance detection and similarity comparison . And then threshold segmentation of the saliency map to get the real target. The small target detection experiments are carried out on infrared source images with different complex backgrounds and different data types. The results show that: compared with the baseline algorithm, the detection results of the proposed algorithm in this paper increase the background suppression factor and the signal-clutter ratio gain by 2-3 times , the intersection of union is the best in the HVS method, and the ROC curve obtains the highest detection accuracy at a lower false alarm rate. The method in this paper effectively fuses multiple characteristics of small targets in the infrared source image, improves the detection accuracy and reduces the complexity of the algorithm, and can still achieve good target positioning and background suppression in the case of different complex backgrounds and clutter interference.