Intent recognition method based on deep learning network

Abstract

The present invention relates to the field of intelligent recognition, and discloses an intent recognition method based on a deep learning network, resolving a technical problem that accuracy of intent recognition is not high. A key point of the technical solutions is migrating features of a first deep learning network to a second deep learning network, mainly including: converting data sets of all fields into a word sequence WS and a corresponding PINYIN sequence PS; meanwhile, manually labeling the data set of a certain field and converting the data set into a word sequence WD, a PINYIN sequence PD, and a label; inputting the word sequence WS and the PINYIN sequence PS to the first deep learning network for training to obtain a language model, initializing and updating an encoding layer parameter matrix of the language model; and weighting and inputting the word sequence WD and the PINYIN sequence PD to the second deep learning network after the word sequence WD and the PINYIN sequence PD are inputted to the second deep learning network to be encoded, to train an intent recognition model. Accuracy of performing intent recognition by using the intent recognition model is higher.