The automatic detection technology for digital radiographic images of launch vehicle welds primarily involves the classification of digital radiographic images of launch vehicle welds. However， the actual production process yields an enormous volume of images， and annotating the entire dataset would entail a considerable waste of manpower and resources. Considering that prior supervisory information can enhance the precision of target extraction and that removing the background from images can improve classification accuracy， this paper proposes a Semi-supervised Target Feature Extraction algorithm with Laplacian Eigenmaps （LE） Regularization based on Robust Principal Component Analysis （RPCA）， termed SSRLE. On the premise of ensuring the global structure of the data， the local structure of the data is guaranteed by adding the LE regularization of the weight matrix of the adaptive neighborhood graphs， and the influence of the nearest neighbor value k in the classical LE algorithm is excluded. Under the influence of prior information， the target and background are separated effectively. The linear classifier is trained with the target data and supervision information. With the manifold smoothing hypothesis， the trained linear classifier can predict unlabeled data， resulting in improved classification results. Finally， experiments are carried out， and the classification effects of different semi-supervised algorithms are compared. The results show that the proposed method is valid， and is superior to other methods.