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The recently occurred representation learning make an attractive performancein NLP and complex network, it is becoming a fundamental technology in machinelearning and data mining. How to use representation learning to improve theperformance of classifiers is a very significance research direction. We usingrepresentation learning technology to map raw data(node of graph) to alow-dimensional feature space. In this space, each raw data obtained a lowerdimensional vector representation, we do some simple linear operations forthose vectors to produce some virtual data, using those vectors and virtualdata to training multi-tag classifier. After that we measured the performanceof classifier by F1 score(Macro% F1 and Micro% F1). Our method make Macro F1rise from 28 % - 450% and make average F1 score rise from 12 % - 224%. Bycontrast, we trained the classifier directly with the lower dimensional vector,and measured the performance of classifiers. We validate our algorithm on threepublic data sets, we found that the virtual data helped the classifier greatlyimprove the F1 score. Therefore, our algorithm is a effective way to improvethe performance of classifier. These result suggest that the virtual datagenerated by simple linear operation, in representation space, still retainsthe information of the raw data. It's also have great significance to thelearning of small sample data sets.

EEMC: Embedding Enhanced Multi-tag Classification