Multi-label classification of technical articles based on deep neural network

This paper uses word-embedding and deep neural networks to build a multi-label classification model based on technical articles. In this paper, we use deep learning algorithms to train word vectors based on numerous technical articles, and then with the abstracts and corresponding CNKI labels of these articles as input of network is trained for compare and research the prediction results of different networks, finally determining the threshold by statistical distribution for label screening. Through parameter tuning, model fusion and data augmentation, the accuracy of multi-tag prediction network reaches 92.05%. Multi-label classification based on deep neural network has advantages in simple preprocessing, high accuracy and computational efficiency.