The brazing quality of metal honeycomb components is usually evaluated by the brazing rate （the proportion of the welded area detected in the unit area） as an indicator. In actual production， the GH4099 superalloy thin-walled narrow-ribbed honeycomb panel is used as the research object， ultrasonic C-scan amplitude imaging is used for non-destructive testing， and an unsupervised machine learning classification method based on the eigenvalue parameters of ultrasonic A-scan signal is proposed. Firstly， eight eigenvalues are extracted in the time domain and power spectrum of the digital ultrasound signal， respectively. Secondly， the data is standardized and reduced the dimensionality by using principal components analysis （PCA） to obtain the top three groups with six principal component values， which have respective contribution rates of more than 95%. Then these values are used as eigenvalues to perform K-means clustering， Gaussian mixture model clustering， and fuzzy C-means clustering as the input. Finally， the multi-classifier fusion algorithm is used to improve the accuracy of the model， and the classification results are visualized and compared with the ultrasound C-scan amplitude imaging to verify the classification evaluation effect. Experimental results of twelve groups of data show that the imaging results of the three clustering algorithms are consistent with those of the ultrasound C-scan amplitude imaging， in which the fusion voting calculation is more accurate than the single classifier. The study provides new ideas for an unsupervised machine learning method in ultrasound signal for evaluating the quality of honeycomb component brazing.