A background modeling method of infrared dim small target based on improved eigenspace is proposed in order to effectively remove the interference of dynamic background on dim small target signal. Firstly, an improved anisotropic filtering algorithm is employed to filter from the spatial perspective to constrain the differences of each component of the image. Then, a feature matrix is formed from the filtered images in the continuous time domain, and the Principal Component Analysis (PCA) is adopted to perform feature decomposition. Finally, the input image is projected onto the eigenspace for background modeling. As to adapt to the dynamic background, the background model is updated with a certain learning rate in temporal domain. Experimental results show that the proposed algorithm achieves better background estimation effect than the traditional algorithm. The structural similarity SSIM, contrast gain I and background suppression factor BIF are greater than 0.97, 15.46 and 5.25 respectively