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On the basis of correlation between four typical bubble status and different ore grades,a new recognition method based on the improved k-means algorithm and integrating priori knowledge is proposed for working condition recognition of antimony flotation.In offline classification stage,firstly extract the bubble feature of images under different bubble status to obtain the datasheet.Then combine with the prior knowledge(number of bubble status),cluster the datasheet of bubble feature with the improved k-means algorithm.At last,classify the working condition and their causes under different bubble status and different ore grade..For the online recognition,firstly determine current bubble status with the clustering centers which obtained in improved k-means algorithm and the k-nearest neighbor algorithm.Then recognize current working condition by the working condition classification and the current ore grade.At last,verify the validity of the recognition in flotation process control by analyzing the concentrate grade under different bubble status.