To solve the problem of lack of human visual characteristics in the non-reference blur image quality assessment. This paper proposes a blur image quality assessment method based on blur detection probability variation. This algorithm firstly preprocesses the image, uses the improved adaptive method to calculate the specific salient threshold of blurred image and binarizes the image with a specific threshold to obtain the final salient region of the image. Then, the image quality is described by the blur detection probability variation of the salient regions of the two images after re-blurring. The larger the change, the clearer the image quality. Experimental results show that the proposed algorithm achieves better experimental results in the LIVE data set and has better evaluation performance than the existing traditional algorithms. At the same time, the proposed algorithm can also be used in the field of wisdom mine and so on.