A computer-implemented method, in the field of machine learning, for classifying data produced by an industrial system during operation to analyse operating conditions occurring in the industrial system, comprising: a) classifying at least two pieces of data, to obtain at least two data clusters wherein the two data clusters are grouped by types of distance and densities of adj acent data; b) determining a bias degree of classification; c) re-classifying the at least two pieces of data by merging any several of the at least two data clusters; d) determining a bias degree of re-classification; and e) determining, by comparing the bias degree of first classification and the bias degree of re-classification, which classification is more accurate; wherein f1) the determining a bias degree of classification comprises: calculating respective first bias estimations of the at least two data clusters, wherein a first bias estimation of any data cluster of the at least two data clusters indicates a difference degree between the any data cluster and output data of a first neural network model of the any data cluster when the any data cluster is input to the first neural network model of the any data cluster, the first neural network model of the any data cluster is obtained through training by using data comprised in the any data cluster, and an input layer and an output layer of the first neural network model have the same quantity of nodes; f2) the determining a bias degree of re-classification comprises: for any several data clusters selected from the at least two data clusters, calculating respective second bias estimations of the any several data clusters, wherein a second bias estimation of each data cluster in the any several data clusters indicates a difference degree between the data cluster and output data of a particular neural network model when the data cluster is input to the particular neural network model, the particular neural network model is obtained through training by using all data comprised in the any several data clusters, and an input layer and an output layer of the particular neural network model have the same quantity of nodes; and f3) the determining which classification is more accurate comprises: determining, based on respective first bias estimations and second bias estimations of the any several data clusters, whether the any several data clusters are related, and marking the any several data clusters as related when a determining result is positive; and further comprising: g) merging data clusters marked as related in the at least two data clusters into a data cluster. a) classifying at least two pieces of data, to obtain at least two data clusters wherein the two data clusters are grouped by types of distance and densities of adj acent data; b) determining a bias degree of classification; c) re-classifying the at least two pieces of data by merging any several of the at least two data clusters; d) determining a bias degree of re-classification; and e) determining, by comparing the bias degree of first classification and the bias degree of re-classification, which classification is more accurate; wherein f1) the determining a bias degree of classification comprises: calculating respective first bias estimations of the at least two data clusters, wherein a first bias estimation of any data cluster of the at least two data clusters indicates a difference degree between the any data cluster and output data of a first neural network model of the any data cluster when the any data cluster is input to the first neural network model of the any data cluster, the first neural network model of the any data cluster is obtained through training by using data comprised in the any data cluster, and an input layer and an output layer of the first neural network model have the same quantity of nodes; f2) the determining a bias degree of re-classification comprises: for any several data clusters selected from the at least two data clusters, calculating respective second bias estimations of the any several data clusters, wherein a second bias estimation of each data cluster in the any several data clusters indicates a difference degree between the data cluster and output data of a particular neural network model when the data cluster is input to the particular neural network model, the particular neural network model is obtained through training by using all data comprised in the any several data clusters, and an input layer and an output layer of the particular neural network model have the same quantity of nodes; and f3) the determining which classification is more accurate comprises: determining, based on respective first bias estimations and second bias estimations of the any several data clusters, whether the any several data clusters are related, and marking the any several data clusters as related when a determining result is positive; and further comprising: g) merging data clusters marked as related in the at least two data clusters into a data cluster.