In order to explore the feasibility of a self-supervised feature extraction method in skeletal X-ray image anomaly detection， an anomaly detection method for skeletal X-ray images based on self-supervised feature extraction was proposed. The self-supervised learning framework and Vision Transformer （ViT） model were combined for feature extraction in skeletal anomaly detection， and anomaly detection classification was carried out by linear classifiers， which can effectively avoid the dependence of supervised models on large-scale labeled data in feature extraction stage. Experiments were performed on publicly available skeletal X-ray image datasets， the skeletal anomaly detection models based on pre-trained Convolutional Neural Network （CNN） and self-supervised feature extraction were evaluated with accuracy. Experimental results show that self-supervised feature extraction model has better effect than the general CNN models， its classification results in seven parts are similar to those of supervised CNN models， but the abnormal detection accuracy for elbow， finger and humerus achieved optimal values， and the average accuracies increases by 5.37 percentage points compared to ResNet50. The proposed method is easy to implement and can be used as a visual assistant tool for radiologist initial diagnosis.