Infrared small target detection technology is one of the key technologies for reconnaissance, guidance, and early warning systems, and it has important theoretical and practical value to conduct in-depth research on it. However, there are several challenges in infrared small target detection. Firstly, infrared small targets have low signal-to-noise ratio, which makes them easily submerged in complex backgrounds. Secondly, since infrared small target detection is a long-distance imaging process, there is no shape or texture information available, which increases the difficulty of target detection. To address these challenges, this paper proposes a multi-level contrast enhancement method to suppress structural background, and develops a more effective detection algorithm. Based on the concept of local contrast measurement (LCM), a new contrast-based small target detection algorithm called Multi-Level Local Contrast Measurement (MLLCM) is constructed, and its effective implementation process is provided. Compared with LCM, MPCM (Multiscale Patch-based Contrast Measure), and other algorithms, this algorithm effectively enhances the target area and eliminates background clutter. The results on simulated images demonstrate the effectiveness of this algorithm.