It is an important and challenging topic to deal with infrared small target detection with high detection rate, low false alarm rate and low computational complexity in various application fields. Aiming at solving the problem that the existing algorithms can not effectively enhance the real foreground target and suppress various complex background interference. This paper proposes an infrared small target detection algorithm based on entropy weighted multiscale local contrast. Firstly, the local contrast formula is redefined in the joint form of ratio and difference, which can enhance the target and suppress the background clutter. Secondly, the local entropy can be used to reflect the gray mutation in the local area of the image. We use the modified local entropy operator to weight the multiscale local contrast. Finally, we employ the adaptive threshold segmentation to separate the target from the background and obtain the final infrared small target. We test six groups of infrared image sequences with different targets and backgrounds, the backgrounds include mountain, forest, field, sea-sky, sky and thick cloud. Experimental results show that, the proposed algorithm can not only robustly detect infrared dim and small targets of different sizes in various complex backgrounds, but also has higher detection efficiency and lower false alarm rate compared with other traditional baseline methods.