Due to the strong complexity and high noise of reflected light interference in laser images, the accuracy and efficiency of multi-level feature point recognition in laser images are low. Therefore, a multi-level feature point recognition study for laser images with reflected light interference is proposed. Calculate the slope of adjacent points based on the characteristics of reflected light interfering with laser images, and screen effective laser data. Using data curvature algorithm to change the distance between reflected light interfering laser images, eliminate effective reflected light interfering laser image noise, and complete filtering and smoothing processing. Using Mask R-CNN to detect the effective laser data noise of filtered reflected light interference laser images, the loss function is used to extract feature point information at different levels of Mask R-CNN, thereby completing multi-level feature point recognition of reflected light interference laser images. The test results show that the proposed method has good multi-level feature point recognition results, and can recognize all multi-level feature points. The maximum misjudgment rate for effective laser data filtering is 0.4%, and the multi-level feature point recognition rate is higher than 95.25%. The maximum recognition time is 22 ms.