CFRP materials have been widely used in the aerospace field due to their advantages such as lightweight, high strength, and processability. Due to the complexity of the surface of aluminum honeycomb filled CFRP structures, there are technical difficulties in extracting the comprehensive features of surface response signals and surface images. In order to more accurately evaluate the health status of CFRP structures, a infrared laser surface damage detection method for aluminum honeycomb filled CFRP structures was studied. Based on the modification of infrared laser equipment, set the working parameters of the equipment, classify the types of infrared laser surface damage in aluminum honeycomb filled CFRP structures, and set standard features for different types of damage surfaces as damage detection standards. Using infrared laser technology to obtain response signals and surface images of CFRP structures, extracting signal and image features, and using feature matching to detect the type of damage in CFRP structures, the detection results of surface damage depth, area, position and other parameters are obtained. The conclusion drawn from performance testing experiments is that the surface damage type misdetection coefficient of the optimized design method is significantly reduced, and the detection errors for damage area and depth are the lowest, with 0.19mm2 and 0.06mm respectively, indicating good detection ability.