A computer-implemented method for the classification of network data for the identification of spam pages, characterized in that it comprises: dynamically updating a number of candidate classifiers, the candidate classifiers constructed using labelled normal data and cheating data, for improving the utilization rate of target classifiers by: determining N candidate classifiers to be updated; obtaining, according to the classification accuracy of each candidate classifier of said N candidate classifiers, a third weight value of each of said N candidate classifiers; obtaining, according to a second assigned time and construction time of each of said N candidate classifiers, a fourth weight value of each of said N candidate classifiers; and removing, according to said third weight value and said fourth weight value, P candidate classifiers from said N candidate classifiers, so as to obtain M target classifiers, with P being an integer which is greater than or equal to 1 and smaller than or equal to N - 2, M being an integer greater than or equal to 2, and each of said M target classifiers being independent of another; inputting network data to be predicted into said M target classifiers respectively, so as to obtain the predicted result output by each target classifier of said M target classifiers; and obtaining, according to the predicted result output by each of said M target classifiers and a prediction weight of each of said M target classifiers, a classification result of said network data, identifying said network data as normal data or cheating data. dynamically updating a number of candidate classifiers, the candidate classifiers constructed using labelled normal data and cheating data, for improving the utilization rate of target classifiers by: determining N candidate classifiers to be updated; obtaining, according to the classification accuracy of each candidate classifier of said N candidate classifiers, a third weight value of each of said N candidate classifiers; obtaining, according to a second assigned time and construction time of each of said N candidate classifiers, a fourth weight value of each of said N candidate classifiers; and removing, according to said third weight value and said fourth weight value, P candidate classifiers from said N candidate classifiers, so as to obtain M target classifiers, with P being an integer which is greater than or equal to 1 and smaller than or equal to N - 2, M being an integer greater than or equal to 2, and each of said M target classifiers being independent of another; determining N candidate classifiers to be updated; obtaining, according to the classification accuracy of each candidate classifier of said N candidate classifiers, a third weight value of each of said N candidate classifiers; obtaining, according to a second assigned time and construction time of each of said N candidate classifiers, a fourth weight value of each of said N candidate classifiers; and removing, according to said third weight value and said fourth weight value, P candidate classifiers from said N candidate classifiers, so as to obtain M target classifiers, with P being an integer which is greater than or equal to 1 and smaller than or equal to N - 2, M being an integer greater than or equal to 2, and each of said M target classifiers being independent of another; inputting network data to be predicted into said M target classifiers respectively, so as to obtain the predicted result output by each target classifier of said M target classifiers; and obtaining, according to the predicted result output by each of said M target classifiers and a prediction weight of each of said M target classifiers, a classification result of said network data, identifying said network data as normal data or cheating data. <EOS>