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Automated classification of citation function in scientific text is a new emerging research topic inspired by traditional citation analysis in applied linguistic and scientometric fields. The aim is to classify citations in scholarly publication in order to identify author’s purpose or motivation for quoting or citing a particular paper. Several citation schemes have been proposed to classify the citations into different functions. However, it is extremely challenging to find standard scheme to classify citations, and some of the proposed schemes have similar functions. Moreover, most of previous studies mainly used classical machine learning methods such as support vector machine and neural networks with a number of manually created features. These features are incomplete and suffer from time-consuming and error prone weakness. To address these problems, we present a new citation scheme with less functions and propose a deep learning model for classification. The citation sentences and author’s information were fed to convolutional neural networks to build citation and author representations. A corpus was built using the proposed scheme and a number of experiments were carried out to assess the model. Experimental results have shown that the proposed approach outperforms the existing methods in term of accuracy, precision and recall.