Focusing on the issue of long delay in detection of copper alloy composition， a classification and recognition method of copper alloy metallograph based on feature aggregation was proposed. Firstly，in the feature extraction stage， the Gray-Level Co-occurrence Matrix （GLCM） and the Residual Network （ResNet） model based on convolutional block attention module were constructed to extract the global and local features of the image， respectively. Secondly， in the feature aggregation stage， the extracted features were normalized and then cascaded in a simple way. Finally， in the classification and recognition stage， a Support Vector Machine （SVM） was used for accurate classification. Experimental results show that the proposed method achieves the accuracy of 98.963% and macro-F1 of 98.996%， which are better than those of machine learning methods based on single feature. It can be seen that the features extracted by different methods can describe the texture and edge information of copper alloy metallographs more comprehensively after aggregation， and the proposed method can identify different copper alloys by metallographs， which improves the accuracy of identification and has good robustness.