Online monitoring of the aircraft skin laser paint removal process is an important means to achieve layered and controllable paint removal and meet airworthiness maintenance requirements. It is also the key technology to promote the industrial application of laser paint removal and aircraft maintenance automation. Currently, the main monitoring methods include surface imaging and process performance parameter measurement methods. However, these methods have inherent limitations, making it difficult to be online and real-time. Laser-induced plasma breakdown spectroscopy (LIBS) technology has the advantages of equipment simplicity, flexibility, quickness and sensitivity, which has been widely used in online monitoring and research of laser cleaning of artworks and cultural relics. Based on the established high-frequency nanosecond infrared pulsed laser paint removal LIBS online monitoring platform, three LIBS spectra (100 frames each) were collected during the removal of topcoat, primer and aluminum alloy substrate under different laser powers. The changes of characteristic spectral lines of various spectral tracer elements under different laser powers were analyzed, and 12 characteristic spectral lines were preliminarily screened as the characteristics of spectral identification. Principal component analysis (PCA) was further performed on these 12 characteristics. The data set composed of the first three principal components (PC1, PC2 and PC3) was used as the input of the support vector machines (SVM) identification model, and the identification model of three types of spectral data was established. A LIBS online monitoring and judgment rule for the controllable removal process of laser layering of multi-paint-layer structure was formed, and the rules validity was experimentally verified. It can be seen from the results that, compared with the needle-like LIBS spectra collected based on low-frequency pulsed laser single-point action, in general, the LIBS spectra collected based on this platform show a strong continuous background (greater than 5 000 a.u.) and a full width at half maximum of about 1.5 nm; an improved mean smoothing filtering algorithm was designed for this type of spectrum, which effectively avoids the intensity distortion of the characteristic spectral line while removing the background spectrum; under different laser powers, the characteristic spectral line of the tracer element is unstable; the contribution of the first three principal components, i.e., PC1, PC2, and PC3 in the principal component analysis to the explanation of the spectral data reaches 95%. The same type of spectra is clustered regionally in the three-dimensional space formed by them. The recognition accuracy of the PCA-SVM model on the training set and test set is 99.44% and 100%, respectively; the verification experimental results show that the identification models of the three types of spectra and the online monitoring and judgment rules are effective. The established identification model and judgment rules can provide key technical support for the monitoring and automation solutions of the aircraft skin laser layered paint removal process.