Abstract: Short text has a small number of vocabularies and weak description of information,resulting in the characteris-tics of high dimensionality,sparse features and noise interference.The existing clustering algorithms have low accuracy and efficiency for the large-scale short text.A short text clustering algorithm based on deep learning convolution neural network was proposed to solve this problem.The proposed clustering algorithm uses the word2vec model to learn the potential semantic association between words in the short text,and the multidimensional vector to represent the single word based on the large-scale corpus,and then the short text is also expressed as the multidimensional original vector form.Using convolution neural network,the feature vector is extracted from the original vector of sparse and high dimension to the low-dimensional text vector with more effective characteristics.Finally,the traditional clustering algorithm is used to cluster the short text.The proposed clustering method is feasible and effective for the reduction of text vector,and has achieved good short text clustering effect with F-measure of over 75%.

Key words: Convolution neural network, Deep learning, Document clustering, Short text, Word2vec