Abstract: The complex subtle differentiated network data need to be extracted for semantic,and it is a key technology to realize the accurate identification and retrieval of Web network data.The network data have nonlinear and random distribution,subject distribution is wide,and the update frequency is fast,so it is difficult to extract.A semantic optimization feature extraction algorithm for network data was proposed based on the Dopplerlet transform projection.The semantic Gauss edge rectangle window function is given,and data fusion system of difference fusion filtering is constructed.The text segmentation is taken,and massive data of information entropy are constructed,which can effectively elimi-nate the abnormal data in the cluster.Self similar characteristics of Dopplerlet transform are used for matching the projection.Nonlinear adaptive matching semantic spectrum feature is extracted,and maximal linearly independent group is searched out at the Hilbert subspace.Simulation results show that the algorithm can increase the feature semantic expression ability,effectively distinguish the differences between redundant data and residual data in network data,and improve the subtle error detection and retrieval capabilities for heterozygous network.

Key words: Dopplerlet transform,Semantics,Feature extraction