A quantum self-organization feature mapping networks model and its clustering algorithm are presented. Both the input and the weight of the model are represented by the quantum bits,and the output of the model is represented by the real number. The model is composed of input layer and competitive layer. Firstly,the samples are transformed into quantum states and transported to the input layer,and then the similar coefficients of quantum states are computed between the input and the weight. Secondly,the competitive layer extracts the implicit pattern characters of the clustering samples and takes self-organization to them,and then output the clustering result. The quantum states of weight are modified by quantum rotation gates. The networks are trained by the algorithm of the unsupervised learning and supervised learning together. Finally two simulation experiments demonstrate that the model and algorithm are evidently superior to the general self-organization feature mapping networks.