The traditional fuzzy morphological associative memories network (FMAM) can not be adaptive to choose the shape and size of structural elements,so there is a problem of misclassification when classifying samples by FMAM.Based on the properties of quantum superposition and collapse,quantum fuzzy morphological associative memories network (QFMAM) was proposed,in which the structural elements were constructed by the qubit system,and the qubit probability represented the corresponding membership degree to obtain adaptive structural elements.The samples were processed with the adaptive structural elements before classified using QFMAM,thereby reducing the influence of useless interference information on classification accuracy.QFMAM was used to classify the simulation data and experimental signals of gear box respectively.It is verified that compared with the classification performance of FMAM,support vector machine (SVM) and naive bayes classifier (NBC),the proposed QFMAM has the advantages of higher training efficiency,stronger learning ability and higher classification accuracy,therefore QFMAM is a very useful intelligent classifier