Research on advertising content recognition based on convolutional neural network and recurrent neural network

The problem to be solved in this paper is to identify the text advertisement information published by users in a medium-sized social networking website. First, the text is segmented and then the text is transformed into sequence tensor by using a word vector representation method, which is input into the deep neural network. Compared with other neural networks, RNN is good at processing training samples with continuous input sequence, and the length of the sequence is different. Although RNN can theoretically solve the training of sequential data beautifully, it has the problem of gradient disappearance, so it is a special LSTM based on RNN model that is widely used in practice. In the experiment, the convolutional neural network is used to process text sequence, and time is regarded as a spatial dimension. Finally, it briefly introduces the use of universal language model fine-tuning for text classification.