Text classification method based on improved long short term memory network

With the booming development of the internet industry and the entry of big data into people's lives, it is particularly important to quickly extract people's needs from massive text information. This article proposes a text classification method based on an improved long and short term memory network. To address the problem of being unable to encode information from back to front, the Bi-LSTM model is improved to concatenate forward and backward outputs with the earliest word vectors to obtain the final word representation, and to find the most important word for classification through the maximum pooling layer, thereby improving the performance of text classification. The experimental results show that the algorithm proposed in this paper has better classification accuracy than other classification algorithms when the number of iterations is large.