A self-supervised anomaly recognition method for wind turbine blades based on multi-channel vibration principal features was proposed, to address the difficulties in the timely and effective identification of abnormal states of wind turbine blade components. In the process of vibration feature construction, the variational mode decomposition-kernel principal component analysis (VMD-kPCA) method was proposed to achieve adaptive feature extraction of multi-channel vibration data in the nacelle. An adaptive bidirectional time-series convolutional network (ABT) was used as the backbone feature extractor to achieve self-supervised regression of vibration principal features, by integrating nacelle vibration with supervisory control and data acquisition (SCADA). A normal behavior model for wind turbine units was constructed based on other SCADA operational variables to eliminate the interference from other factors on the nacelle vibration features. High-precision blade anomaly identification was achieved using a residual sequence detection strategy based on the Mahalanobis distance. In terms of experimental validation, an actual dataset from a leading wind farm company was utilized to test the algorithm’s performance. Results showed that the proposed model had an accuracy rate higher than 90% for detecting three types of abnormalities: blade icing, blade oscillation, and blade tower sweeping, which was superior to other comparative models.